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NLPerspectives","10.63317\u002F5a3bvdkzb6f7","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnlperspectives\u002F2026.nlperspectives-1.0.pdf",[2341,2342,2343,2344,2347],{"given_name":1533,"surname":1534},{"given_name":1524,"surname":1525},{"given_name":1527,"surname":1528},{"given_name":2345,"surname":2346},"Elisa","Leonardelli",{"given_name":1536,"surname":1537},{"workshop_id":2349,"year":7,"full_workshop_id":2350,"proceedings_title":2351,"paperCount":485,"doi":2352,"pdf_url":2353,"venue_ids":2354,"publisher":13,"editors":2355,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"nonliteral","lrec2026_ws_nonliteral","Proceedings of Learning Non-Literal Expressions with Small Data @ LREC 2026","10.63317\u002F24t598e89qez","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnonliteral\u002F2026.nonliteral-1.0.pdf","nonliteral|ws",[2356,2359],{"given_name":2357,"surname":2358},"Markus","Egg",{"given_name":2360,"surname":2361},"Valia","Kordoni",{"workshop_id":2363,"year":7,"full_workshop_id":2364,"proceedings_title":2365,"paperCount":2366,"doi":2367,"pdf_url":2368,"venue_ids":2369,"publisher":13,"editors":2370,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"nslp","lrec2026_ws_nslp","Proceedings of Natural Scientific Language Processing (NSLP) @ LREC 2026",29,"10.63317\u002F44i27tid8nim","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnslp\u002F2026.nslp-1.0.pdf","nslp|ws",[2371,2372,2375,2378,2381],{"given_name":717,"surname":718},{"given_name":2373,"surname":2374},"Stefan","Dietze",{"given_name":2376,"surname":2377},"Danilo","Dessi",{"given_name":2379,"surname":2380},"Diana","Maynard",{"given_name":2382,"surname":2383},"Sonja","Schimmler",{"workshop_id":794,"year":7,"full_workshop_id":2385,"proceedings_title":2386,"paperCount":2387,"doi":2388,"pdf_url":2389,"venue_ids":1548,"publisher":13,"editors":2390,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"lrec2026_ws_osact","The 7th Workshop on Open-Source Arabic Corpora and Processing Tools (OSACT7) with 5 Shared Tasks",43,"10.63317\u002F55nvfe53k6fq","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fosact\u002F2026.osact-1.0.pdf",[2391,2392,2393],{"given_name":1551,"surname":1552},{"given_name":2073,"surname":2074},{"given_name":2394,"surname":2395},"Saad","Ezzini",{"workshop_id":800,"year":7,"full_workshop_id":2397,"proceedings_title":2398,"paperCount":541,"doi":2399,"pdf_url":2400,"venue_ids":1568,"publisher":13,"editors":2401,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"lrec2026_ws_parlaclarin","Proceedings of the ParlaCLARIN V Workshop on Interoperability, Multilinguality, and Multimodality in Parliamentary Corpora","10.63317\u002F2gcgvfpyafm6","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fparlaclarin\u002F2026.parlaclarin-1.0.pdf",[2402,2403,2406],{"given_name":373,"surname":1574},{"given_name":2404,"surname":2405},"Vincent","Vandeghinste",{"given_name":1576,"surname":2407},"Bodron",{"workshop_id":990,"year":7,"full_workshop_id":2409,"proceedings_title":2131,"paperCount":2410,"doi":2411,"pdf_url":2412,"venue_ids":1583,"publisher":13,"editors":2413,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"lrec2026_ws_politicalnlp",30,"10.63317\u002F382p55orpsvc","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fpoliticalnlp\u002F2026.politicalnlp-1.0.pdf",[],{"workshop_id":2415,"year":7,"full_workshop_id":2416,"proceedings_title":2417,"paperCount":815,"doi":2418,"pdf_url":2419,"venue_ids":2420,"publisher":13,"editors":2421,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"pressmint","lrec2026_ws_pressmint","Proceedings of the First Workshop on Creating Interoperable Corpora of Historical Newspapers","10.63317\u002F4xmf6mt4ovnj","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fpressmint\u002F2026.pressmint-1.0.pdf","pressmint|ws",[2422,2425,2428],{"given_name":2423,"surname":2424},"Maciej","Ogrodniczuk",{"given_name":2426,"surname":2427},"Petya","Osenova",{"given_name":2429,"surname":2430},"Tanja","Wissik",{"workshop_id":806,"year":7,"full_workshop_id":2432,"proceedings_title":2433,"paperCount":469,"doi":2434,"pdf_url":2435,"venue_ids":1602,"publisher":13,"editors":2436,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"lrec2026_ws_rail","Proceedings of Resources for African Indigenous Languages (RAIL) 2026 @ LREC 2026","10.63317\u002F44hkfj5cg3wf","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Frail\u002F2026.rail-1.0.pdf",[],{"workshop_id":2438,"year":7,"full_workshop_id":2439,"proceedings_title":2440,"paperCount":645,"doi":2441,"pdf_url":2442,"venue_ids":2443,"publisher":13,"editors":2444,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"rapid6mentalai","lrec2026_ws_rapid6mentalai","Proceedings of the Sixth Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive\u002Fpsychiatric\u002Fdevelopmental impairments in cooperation with the MENTAL.ai consortium","10.63317\u002F54scnv3cy8x7","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Frapid6mentalai\u002F2026.rapid6mentalai-1.0.pdf","rapid6mentalai|ws",[2445,2446,2448,2451,2452,2454],{"given_name":1624,"surname":1625},{"given_name":1630,"surname":2447},"Themistocleous",{"given_name":2449,"surname":2450},"Gaël","Dias",{"given_name":1627,"surname":1628},{"given_name":1639,"surname":2453},"Öhman",{"given_name":2455,"surname":2456},"Sebastião","Pais",{"workshop_id":2458,"year":7,"full_workshop_id":2459,"proceedings_title":2460,"paperCount":712,"doi":2461,"pdf_url":2462,"venue_ids":2463,"publisher":13,"editors":2464,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"readixtsar","lrec2026_ws_readixtsar","Proceedings of the Joint Workshop on Readability and Text Simplification (READIxTSAR) @ LREC 2026","10.63317\u002F3odyoa9tpigg","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Freadixtsar\u002F2026.readixtsar-1.0.pdf","readixtsar|ws",[2465,2468,2470,2472,2474,2475,2478,2481,2484,2485],{"given_name":2466,"surname":2467},"Matthew","Shardlow",{"given_name":1723,"surname":2469},"François",{"given_name":384,"surname":2471},"Amaro",{"given_name":690,"surname":2473},"Baptista",{"given_name":1652,"surname":1653},{"given_name":2476,"surname":2477},"Eugénio","Ribeiro",{"given_name":2479,"surname":2480},"Horacio","Saggion",{"given_name":2482,"surname":2483},"Regina","Stodden",{"given_name":1655,"surname":1656},{"given_name":1650,"surname":1649},{"workshop_id":2487,"year":7,"full_workshop_id":2488,"proceedings_title":2489,"paperCount":678,"doi":2490,"pdf_url":2491,"venue_ids":2492,"publisher":13,"editors":2493,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"resourceful","lrec2026_ws_resourceful","The Fourth Workshop on Resources and Representations for Under-Resourced Languages and Domains (RESOURCEFUL 2026)","10.63317\u002F3mcee7ktdfxn","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fresourceful\u002F2026.resourceful-1.0.pdf","resourceful|ws",[],{"workshop_id":826,"year":7,"full_workshop_id":2495,"proceedings_title":2496,"paperCount":836,"doi":2497,"pdf_url":2498,"venue_ids":2499,"publisher":13,"editors":2500,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"lrec2026_ws_signlang","Proceedings of the LREC 2026 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion","10.63317\u002F4zjm486botgq","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fsignlang\u002F2026.signlang-1.0.pdf","signlang|ws",[2501,2502,2503,2504,2507,2508],{"given_name":1717,"surname":1716},{"given_name":1720,"surname":1719},{"given_name":1723,"surname":1722},{"given_name":2505,"surname":2506},"Julie","A. Hochgesang",{"given_name":1729,"surname":1728},{"given_name":616,"surname":1731},{"workshop_id":1030,"year":7,"full_workshop_id":2510,"proceedings_title":2511,"paperCount":957,"doi":2512,"pdf_url":2513,"venue_ids":1738,"publisher":13,"editors":2514,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"lrec2026_ws_sigul","Proceedings of the SIGUL 2026 Joint Workshop with ELE, EURALI, and DCLRL \"Towards Inclusivity and Equality: Language Resources and Technologies for Under-Resourced and Endangered Languages","10.63317\u002F3x5d49bm2yjm","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fsigul\u002F2026.sigul-1.0.pdf",[2515,2516,2517,2518,2519,2520,2523,2524,2525],{"given_name":1246,"surname":1247},{"given_name":67,"surname":68},{"given_name":1745,"surname":1746},{"given_name":1741,"surname":1742},{"given_name":687,"surname":688},{"given_name":2521,"surname":2522},"Constantine","Lignos",{"given_name":1256,"surname":1257},{"given_name":1772,"surname":2167},{"given_name":717,"surname":718},{"workshop_id":2527,"year":7,"full_workshop_id":2528,"proceedings_title":2529,"paperCount":771,"doi":2530,"pdf_url":2531,"venue_ids":2532,"publisher":13,"editors":2533,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"slide","lrec2026_ws_slide","Proceedings of the Workshop on Structured Linguistic Data and Evaluation (SLiDE)","10.63317\u002F2ncrhaxfvhi4","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fslide\u002F2026.slide-1.0.pdf","slide|ws",[2534,2537,2540,2543,2546],{"given_name":2535,"surname":2536},"Germany)","Erhard Hinrichs (Tübingen University",{"given_name":2538,"surname":2539},"Sweden)","Joakim Nivre (Uppsala University",{"given_name":2541,"surname":2542},"Bulgaria)","Petya Osenova (Sofia University",{"given_name":2544,"surname":2545},"USA)","James Pustejovsky (Brandeis University",{"given_name":2535,"surname":2547},"Claus Zinn (Tübingen University",{"workshop_id":2549,"year":7,"full_workshop_id":2550,"proceedings_title":2551,"paperCount":469,"doi":2552,"pdf_url":2553,"venue_ids":2554,"publisher":13,"editors":2555,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"soconnlpsi","lrec2026_ws_soconnlpsi","Proceedings of the 1st Workshop on Social Context (SoCon) and the 2nd Workshop on Integrating NLP and Psychology to Study Social Interactions (NLPSI) @ LREC 2026","10.63317\u002F5qbp9pb9xpfe","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fsoconnlpsi\u002F2026.soconnlpsi-1.0.pdf","soconnlpsi|ws",[2556,2558,2559,2562,2563,2566,2569,2572,2573,2574,2577,2578,2581,2584,2587],{"given_name":1433,"surname":2557},"Antonio Stranisci",{"given_name":1984,"surname":1985},{"given_name":2560,"surname":2561},"Sofie","Labat",{"given_name":2126,"surname":2127},{"given_name":2564,"surname":2565},"Aswathy","Velutharambath",{"given_name":2567,"surname":2568},"Sabine","Weber",{"given_name":2570,"surname":2571},"Rossana","Damiano",{"given_name":1536,"surname":1537},{"given_name":61,"surname":62},{"given_name":2575,"surname":2576},"Bennett","Kleinberg",{"given_name":474,"surname":475},{"given_name":2579,"surname":2580},"Viviana","Patti",{"given_name":2582,"surname":2583},"Flor","Miriam Plaza-del-Arco",{"given_name":2585,"surname":2586},"Maarten","Sap",{"given_name":2588,"surname":2589},"Seid","Muhie Yimam",{"workshop_id":2591,"year":7,"full_workshop_id":2592,"proceedings_title":2593,"paperCount":771,"doi":2594,"pdf_url":2595,"venue_ids":2596,"publisher":13,"editors":2597,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"speakable","lrec2026_ws_speakable","Proceedings of Speech Language Models in Low-Resource Settings: Performance, Evaluation, and Bias Analysis (SPEAKABLE) @ LREC 2026","10.63317\u002F443zrkx8bhr6","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fspeakable\u002F2026.speakable-1.0.pdf","speakable|ws",[2598,2601,2604,2606,2609,2611],{"given_name":2599,"surname":2600},"Nina","Hosseini-Kivanani",{"given_name":2602,"surname":2603},"Alessio","Brutti",{"given_name":1433,"surname":2605},"Matassoni",{"given_name":2607,"surname":2608},"Sandipana","Dowerah",{"given_name":1530,"surname":2610},"Liga",{"given_name":2612,"surname":2613},"Christoph","Schommer",{"workshop_id":2615,"year":7,"full_workshop_id":2616,"proceedings_title":2617,"paperCount":2366,"doi":2618,"pdf_url":2619,"venue_ids":2620,"publisher":13,"editors":2621,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"udw","lrec2026_ws_udw","Proceedings of the Ninth Workshop on Universal            Dependencies (UDW 2026)","10.63317\u002F4c2x4v6ohrvs","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fudw\u002F2026.udw-1.0.pdf","udw|ws",[],{"workshop_id":859,"year":7,"full_workshop_id":2623,"proceedings_title":2624,"paperCount":628,"doi":2625,"pdf_url":2626,"venue_ids":1821,"publisher":13,"editors":2627,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},"lrec2026_ws_wildre","Proceedings of the 8th Workshop on Indian Language Data: Resources and Evaluation","10.63317\u002F32ouujp5bxoa","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fwildre\u002F2026.wildre-1.0.pdf",[2628,2629,2630,2632],{"given_name":1824,"surname":1825},{"given_name":552,"surname":553},{"given_name":1827,"surname":2631},"L",{"given_name":2633,"surname":1783},"Devendr",{"conference_id":6,"year":7,"proceedings_title":8,"venue_ids":9,"isbn":10,"issn":11,"doi":12,"publisher":13,"editors":2635,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40,"conference_url":41,"pdf_url":42,"img_conf_url":43,"paperCount":44},[2636,2637,2638,2639,2640,2641],{"given_name":16,"surname":17},{"given_name":19,"surname":20},{"given_name":22,"surname":23},{"given_name":25,"surname":26},{"given_name":28,"surname":29},{"given_name":31,"surname":32},{"workshop":2643,"papers":2654},{"workshop_id":2264,"year":7,"full_workshop_id":2265,"proceedings_title":2266,"paperCount":2267,"doi":2268,"pdf_url":2269,"venue_ids":2270,"publisher":13,"editors":2644,"conference_name":33,"conference_acronym":34,"conference_number":35,"conference_location":36,"conference_city":37,"conference_country":38,"conference_start_date":39,"conference_end_date":40},[2645,2646,2647,2648,2649,2650,2651,2652,2653],{"given_name":2273,"surname":2274},{"given_name":2073,"surname":2074},{"given_name":2277,"surname":2278},{"given_name":2280,"surname":2281},{"given_name":2283,"surname":2284},{"given_name":2286,"surname":2287},{"given_name":1111,"surname":2082},{"given_name":2290,"surname":2291},{"given_name":2293,"surname":2294},[2655,2677,2693,2713,2733,2755,2776,2809,2832,2850,2871,2888,2902,2916,2927,2953,2970,2986,2998,3015,3031,3054,3072,3092,3106,3123,3152,3175,3195,3215,3231,3245,3259,3273,3290,3309,3327,3348,3372,3391,3405,3448,3469,3483,3500,3515,3529],{"paper_id":2656,"title":2657,"year":7,"month":358,"day":135,"doi":2658,"resource_url":2659,"first_page":459,"last_page":2660,"pdf_url":2661,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2662,"paper_type":2663,"authors":2664,"abstract":2676},"lrec2026-ws-nakbanlp-01","The NakbaEcho Dataset: From Oral Testimonies to a Transcribed Arabic History Corpus ","10.63317\u002F4zvtrpg8sm2s","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-01","22","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.1.pdf","balah-etal-2026-nakbaecho","workshop",[2665,2668,2671,2673],{"paper_id":2656,"author_seq":459,"given_name":2666,"surname":2667,"affiliation":135,"orcid":135},"Batool Najeh","Balah",{"paper_id":2656,"author_seq":434,"given_name":2669,"surname":2670,"affiliation":135,"orcid":135},"Mahmoud","Fawzi",{"paper_id":2656,"author_seq":408,"given_name":1589,"surname":2672,"affiliation":135,"orcid":135},"Elmimouni",{"paper_id":2656,"author_seq":387,"given_name":2674,"surname":2675,"affiliation":135,"orcid":135},"Walid","Magdy","We present NakbaEcho, a dataset derived from Palestinian testimonies about the 1948 Nakba. The resource is constructed from transcribing over 2,180 hours of recorded interviews gathered through the Palestine Remembered Oral History index and linked to multiple repositories, including the Palestinian Oral History Archive (POHA) and YouTube-hosted interviews. We harmonize interview-level metadata and generate timestamp-aligned transcripts from the original Arabic recordings using an automatic transcription pipeline configured for Palestinian Arabic. The dataset includes speaker-labeled segments and auxiliary annotations designed to support downstream research in Arabic speech processing, natural language processing, digital humanities, and oral-history analysis. NakbaEcho contributes a structured computational resource for studying Palestinian oral testimony while expanding the availability of dialectal Arabic materials for speech, text, and social research.",{"paper_id":2678,"title":2679,"year":7,"month":358,"day":135,"doi":2680,"resource_url":2681,"first_page":2682,"last_page":2683,"pdf_url":2684,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2685,"paper_type":2663,"authors":2686,"abstract":2692},"lrec2026-ws-nakbanlp-02","Mining the Pre-1948 Palestinian Press: Unsupervised Keyphrase Extraction and Temporal Discourse Analysis from Five Historical Arabic Newspapers ","10.63317\u002F3oaq8zxp9hwx","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-02","23","32","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.2.pdf","barakat-etal-2026-mining",[2687,2690],{"paper_id":2678,"author_seq":459,"given_name":2688,"surname":2689,"affiliation":135,"orcid":135},"Basel","Barakat",{"paper_id":2678,"author_seq":434,"given_name":2691,"surname":2689,"affiliation":135,"orcid":135},"Nizam","The Palestinian Arabic-language press of the late Ottoman and British Mandate periods constitutes a rich but computationally under-explored archive for studying the evolution of political, cultural, and social discourse in pre-1948 Palestine. This paper presents an end-to-end pipeline for extracting and analyzing thematic content from five historically significant Palestinian newspapers: Lisān al-ʿArab (اﻟﻌﺮب ﻟﺴﺎن), Al-Bushrā (اﻟﺒﺸﺮى), Al-Karmil (اﻟﻜﺮﻣﻞ), Al-Difāʿ (اﻟﺪﻓﺎع), and Filasṭīn (ﻓﻠﺴﻄﻴﻦ). We describe (i) the construction of a five-source corpus from scanned newspaper images obtained from archival collections, processed using the Google Cloud Vision OCR (GCV-OCR) API, (ii) the adaptation of KeyBERT with an AraBERT backbone for unsupervised keyphrase extraction, and (iii) a purpose-built Python visualization toolkit that produces keyword-frequency heatmaps, longitudinal trend charts, and ranked bar charts with full Arabic script rendering. Experiments across the five subcorpora show that the pipeline yields topically diverse keyphrases reflecting each newspaper’s editorial orientation and its distinct representation of Palestinian native perspectives—from pan-Arab nationalism and anti-colonial resistance to religious and communal affairs. Temporal analysis reveals event-responsive patterns that align with major historical developments, including the 1936–1939 Arab Revolt and the intensification of sovereignty discourse toward 1948. The pipeline, data format specifications, and visualization code are provided as supplementary material.",{"paper_id":2694,"title":2695,"year":7,"month":358,"day":135,"doi":2696,"resource_url":2697,"first_page":2698,"last_page":2699,"pdf_url":2700,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2701,"paper_type":2663,"authors":2702,"abstract":2712},"lrec2026-ws-nakbanlp-03","Nakba Discourse 2025: A Bilingual Social Media Dataset for Collective Trauma Analysis ","10.63317\u002F26i8wdd5eyrt","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-03","33","42","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.3.pdf","zaghouani-etal-2026-nakba",[2703,2706,2709],{"paper_id":2694,"author_seq":459,"given_name":2704,"surname":2705,"affiliation":135,"orcid":135},"Wajdi","Zaghouani",{"paper_id":2694,"author_seq":434,"given_name":2707,"surname":2708,"affiliation":135,"orcid":135},"Mabrouka","Bessghaier",{"paper_id":2694,"author_seq":408,"given_name":2710,"surname":2711,"affiliation":135,"orcid":135},"Kais","Attia","We introduce Nakba Discourse 2025, a bilingual full-year social media dataset capturing Arabic and English discourse about the 1948 Palestinian Nakba across Twitter\u002FX and Facebook from January to December 2025. The corpus contains 70,312 unique posts organized into intersecting sub-corpora by language, sentiment, gender, geography, and platform, with engagement metadata and automatically extracted rhetorical features. Analyses reveal systematic variation in engagement and framing across communities. Per-post engagement is highest in Israel and UK subsets (50.62 and 49.08 average likes respectively), while Arabic-language discourse shows markedly lower per-post engagement. Sentiment distribution is strongly skewed, with negative sentiment posts outnumbering positive ones at an 11:1 ratio (54,424 vs. 4,827 posts). Despite dramatic variation in absolute engagement levels, virality rates remain structurally constant at approximately 10% across all Twitter\u002FX sub-corpora, regardless of language, gender, or geography, pointing to platform-level amplification regularities. Gender analysis reveals that women achieve proportional virality equal to men despite producing roughly one-third the volume of posts. Temporal patterns align with cultural calendars, including Thursday peaks associated with Jumu’ah across Arabic and female subsets, and Sunday peaks in English-language subsets reflecting Western media cycles. The dataset will be released for research use and supports multilingual stance detection, virality modeling, rhetorical analysis, and computational studies of digital political memory.",{"paper_id":2714,"title":2715,"year":7,"month":358,"day":135,"doi":2716,"resource_url":2717,"first_page":2718,"last_page":2719,"pdf_url":2720,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2721,"paper_type":2663,"authors":2722,"abstract":2732},"lrec2026-ws-nakbanlp-04","ChronoLearn: A GRAG LLM-Based System for Structuring and Exploring Historical Narratives ","10.63317\u002F2bbamet766xn","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-04","43","49","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.4.pdf","aladdasi-etal-2026-chronolearn",[2723,2726,2729],{"paper_id":2714,"author_seq":459,"given_name":2724,"surname":2725,"affiliation":135,"orcid":135},"Mohammad O.","ALADDASI",{"paper_id":2714,"author_seq":434,"given_name":2727,"surname":2728,"affiliation":135,"orcid":135},"Shahd L.","Abu Hijleh",{"paper_id":2714,"author_seq":408,"given_name":2730,"surname":2731,"affiliation":135,"orcid":135},"Omar","Qawasmeh","ChronoLearn is a KG–LLM framework to structure and ex- plore Arabic historical narratives. It transforms unstructured texts into knowledge graphs using an ETL-based NLP pipeline for entity and re- lation extraction, followed by schema-guided graph construction. The system integrates graph retrieval with LLM generation (GRAG) to pro- duce grounded, explainable narratives and support semantic querying. The approach is evaluated in heterogeneous Palestinian and Jordanian sources, including Nakba-related content, using both quantitative met- rics and comparative analysis. The results demonstrate improved factual grounding and structured reasoning, addressing limitations of text-only approaches in the processing of historical knowledge in Arabic.",{"paper_id":2734,"title":2735,"year":7,"month":358,"day":135,"doi":2736,"resource_url":2737,"first_page":2738,"last_page":2739,"pdf_url":2740,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2741,"paper_type":2663,"authors":2742,"abstract":2754},"lrec2026-ws-nakbanlp-05","Credibility Assessment for Arabic News on the Gaza War: A Hybrid Neural-Symbolic Pipeline ","10.63317\u002F2hvdmcb8xkoh","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-05","50","59","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.5.pdf","abril-etal-2026-credibility",[2743,2746,2749,2752],{"paper_id":2734,"author_seq":459,"given_name":2744,"surname":2745,"affiliation":135,"orcid":135},"Sanaa","Abril",{"paper_id":2734,"author_seq":434,"given_name":2747,"surname":2748,"affiliation":135,"orcid":135},"Sihame","Mouanid",{"paper_id":2734,"author_seq":408,"given_name":2750,"surname":2751,"affiliation":135,"orcid":135},"El habib","Ben lahmar",{"paper_id":2734,"author_seq":387,"given_name":2730,"surname":2753,"affiliation":135,"orcid":135},"Zahour","While misinformation has long circulated online, the Gaza conflict has intensified its visibility and spread across news websites, online portals, and social media, complicating the credibility and long-term curation of conflict-related Arabic records, including historical accounts and written testimonies. This work proposes a hybrid framework for Arabic fake news detection that combines interpretable linguistic cues with contextual semantic representations. The approach integrates fuzzy logic-based handcrafted features capturing exaggerated and sensational linguistic patterns, AraBERT contextual embeddings for semantic understanding, and a CNN-based text feature extractor for local textual patterns. These complementary features are combined into a unified representation for downstream classification. Multiple machine learning and deep learning classifiers are evaluated to identify the most effective detection model. The resulting system is deployed as a real-time web browser plugin, enabling users to automatically assess the credibility of Arabic news content during browsing",{"paper_id":2756,"title":2757,"year":7,"month":358,"day":135,"doi":2758,"resource_url":2759,"first_page":2760,"last_page":2761,"pdf_url":2762,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2763,"paper_type":2663,"authors":2764,"abstract":2775},"lrec2026-ws-nakbanlp-06","Tarikhi: Arabic Temporal Information Extraction from Arabic Historical Documents ","10.63317\u002F5eco9hgcoxp8","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-06","60","69","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.6.pdf","abdo-etal-2026-tarikhi",[2765,2768,2769,2772],{"paper_id":2756,"author_seq":459,"given_name":2766,"surname":2767,"affiliation":135,"orcid":135},"Qusay","Abdo",{"paper_id":2756,"author_seq":434,"given_name":2280,"surname":2281,"affiliation":135,"orcid":135},{"paper_id":2756,"author_seq":408,"given_name":2770,"surname":2771,"affiliation":135,"orcid":135},"Tariq","Sraiji",{"paper_id":2756,"author_seq":387,"given_name":2773,"surname":2774,"affiliation":135,"orcid":135},"Adnan","Saeed","Arabic historical books and archival materials contain rich accounts of political, social, and cultural events, yet they remain largely underutilized computationally due to the scarcity of dedicated Arabic information extraction tools. The challenge is amplified in long-form, scanned historical documents, where optical character recognition noise, orthographic variation, and complex narrative structures complicate automatic processing. In this paper, we present Tarikhi, a retrieval-augmented generation framework for structured temporal event extraction from Arabic scanned books. The proposed pipeline integrates high-accuracy optical character recognition, chunking-based processing for long-document handling, Arabic named entity recognition, span refinement, and a retrieval-enhanced attribute extraction module that identifies event dates, locations, and descriptive summaries. Extracted events are consolidated and linked using semantic and temporal similarity measures, and linked through relation classification to construct structured temporal events. Evaluation on a selected part of modern Arabic historical books demonstrates the feasibility of temporal event extraction from long-form Arabic texts, achieving a 75.3% F1-score under dual human verification. Tarikhi represents a step toward scalable temporal knowledge construction for Arabic digital humanities resources.",{"paper_id":2777,"title":2778,"year":7,"month":358,"day":135,"doi":2779,"resource_url":2780,"first_page":2781,"last_page":2782,"pdf_url":2783,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2784,"paper_type":2663,"authors":2785,"abstract":2808},"lrec2026-ws-nakbanlp-07","NAKBA NLP 2026: Shared Task on Arabic Handwritten Manuscript Understanding (Palestine Memory–Omar Al-Saleh Memoir) ","10.63317\u002F3iakmct86er7","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-07","70","79","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.7.pdf","hamoud-etal-2026-nakba",[2786,2789,2792,2795,2798,2799,2802,2805],{"paper_id":2777,"author_seq":459,"given_name":2787,"surname":2788,"affiliation":135,"orcid":135},"Hadi","Hamoud",{"paper_id":2777,"author_seq":434,"given_name":2790,"surname":2791,"affiliation":135,"orcid":135},"Ahmad Ali","Chamseddine",{"paper_id":2777,"author_seq":408,"given_name":2793,"surname":2794,"affiliation":135,"orcid":135},"Bilal","Shalash",{"paper_id":2777,"author_seq":387,"given_name":2796,"surname":2797,"affiliation":135,"orcid":135},"Firas","Ben Abid",{"paper_id":2777,"author_seq":358,"given_name":2273,"surname":2274,"affiliation":135,"orcid":135},{"paper_id":2777,"author_seq":333,"given_name":2800,"surname":2801,"affiliation":135,"orcid":135},"Chadi","Abou Chakra",{"paper_id":2777,"author_seq":309,"given_name":2803,"surname":2804,"affiliation":135,"orcid":135},"Bernard","Ghanem",{"paper_id":2777,"author_seq":280,"given_name":2806,"surname":2807,"affiliation":135,"orcid":135},"Fadi A.","Zaraket","Transcribing historical Arabic manuscripts into machine-readable text is essential for preserving cultural heritage and enabling computational research in the humanities, yet it remains a challenging task due to handwriting variability, page degradation, and the complexity of Arabic script. To advance research in this area, we introduce the NAKBA NLP 2026 shared task on Arabic manuscript understanding, comprising two complementary tracks: a manual transcription track, in which participating teams annotate unlabelled handwritten line images, and an automatic system track for handwritten text recognition (HTR). Both tracks use the Omar Al-Saleh Memoir Collection, a corpus of 6,395 scanned pages and approximately 1.6 million words, written between 1951 and 1965 and provided by the Palestine Memory Project. The dataset, evaluation scripts, and system outputs are publicly available.[1] In Subtask 1 (Transcription Track), three teams contributed manual line-level transcriptions; evaluation on hidden ground-truth samples yielded Character Error Rates (CER) between 0.06 and 0.11. In Subtask 2 (Systems Track), seven teams submitted HTR systems. The top-performing system, by Misraj AI, achieved a corpus-level CER of 0.079 and Word Error Rate (WER) of 0.244, outperforming the organiser baseline (CER 0.368, WER 0.691). Rankings shift between corpus-level and per-line evaluation: the 3reeq team achieved the lowest per-line CER (0.082). All contributed transcriptions and system outputs are released under CC-BY-4.0 to support continued research in Arabic manuscript recognition and digital humanities.  [1] https:\u002F\u002Facr.ps\u002F1L9BaeY",{"paper_id":2810,"title":2811,"year":7,"month":358,"day":135,"doi":2812,"resource_url":2813,"first_page":2814,"last_page":2815,"pdf_url":2816,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2817,"paper_type":2663,"authors":2818,"abstract":2831},"lrec2026-ws-nakbanlp-08","StanceNakba Shared Task: Actor and Topic-Aware Stance Detection in Public Discourse ","10.63317\u002F3jpmi9oc23t4","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-08","80","90","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.8.pdf","aldous-etal-2026-stancenakba",[2819,2822,2825,2826,2829,2830],{"paper_id":2810,"author_seq":459,"given_name":2820,"surname":2821,"affiliation":135,"orcid":135},"Kholoud Khalil","Aldous",{"paper_id":2810,"author_seq":434,"given_name":2823,"surname":2824,"affiliation":135,"orcid":135},"Md. Rafiul","Biswas",{"paper_id":2810,"author_seq":408,"given_name":2707,"surname":2708,"affiliation":135,"orcid":135},{"paper_id":2810,"author_seq":387,"given_name":2827,"surname":2828,"affiliation":135,"orcid":135},"Shimaa Amer","Ibrahim",{"paper_id":2810,"author_seq":358,"given_name":2710,"surname":2711,"affiliation":135,"orcid":135},{"paper_id":2810,"author_seq":333,"given_name":2704,"surname":2705,"affiliation":135,"orcid":135},"We present StanceNakba 2026, a shared task on stance detection in polarized social media discourse related to the Palestinian-Israeli conflict, organized as part of Nakba-NLP 2026 at LREC-COLING 2026. The task introduces two subtasks: Subtask A (Actor-Level Stance Detection), which classifies English social media posts as Pro-Palestine, Pro-Israel, or Neutral; and Subtask B (Cross-Topic Stance Detection), which identifies Favor, Against, or Neither stances in Arabic posts toward two conflict-related topics, normalization with Israel and refugee presence in Jordan. The task is grounded in an annotated dataset of 2,606 social media posts. A total of 7 teams participated in Subtask A, and 6 teams in Subtask B. Participating systems primarily fine-tuned Arabic and multilingual transformer-based models, including MARBERT, AraBERT, and DeBERTa-v3 variants, with several teams employing cross-validation, ensemble methods, and topic-conditioned architectures. The best-performing systems achieved a Macro F1 of 0.9620 on Subtask A and 0.8724 on Subtask B, demonstrating that transformer-based approaches are highly effective for conflict-domain stance detection while highlighting persistent challenges in cross-topic generalization and neutral class prediction",{"paper_id":2833,"title":2834,"year":7,"month":358,"day":135,"doi":2835,"resource_url":2836,"first_page":2837,"last_page":2838,"pdf_url":2839,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2840,"paper_type":2663,"authors":2841,"abstract":2849},"lrec2026-ws-nakbanlp-09","The NakbaArchiveClassifier Shared Task on Nakba Image Classification ","10.63317\u002F55jks599ezv8","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-09","91","97","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.9.pdf","abrahams-etal-2026-nakbaarchiveclassifier",[2842,2845,2846,2847],{"paper_id":2833,"author_seq":459,"given_name":2843,"surname":2844,"affiliation":135,"orcid":135},"Alexei","Abrahams",{"paper_id":2833,"author_seq":434,"given_name":2283,"surname":2284,"affiliation":135,"orcid":135},{"paper_id":2833,"author_seq":408,"given_name":2273,"surname":2274,"affiliation":135,"orcid":135},{"paper_id":2833,"author_seq":387,"given_name":424,"surname":2848,"affiliation":135,"orcid":135},"Mikros","The proliferation of social media platforms has significantly reshaped how conflicts are documented, generating large-scale visual records that must be structured to enable meaningful analysis. In this paper, we present the NakbaArchiveClassifier shared task, which focuses on binary classification of infrastructure damage in images from Gaza. This task formed part of the Nakba-NLP Workshop at LREC 2026 and is grounded in an ongoing initiative focused on humanitarian archiving. It utilizes a carefully curated dataset of 2,001 images sourced from Palestinian journalists and content creators on Instagram, spanning the period from October 7, 2023 to December 15, 2025. The objective for participants was to classify whether an image depicts damaged or destroyed infrastructure versus intact structures. This task poses multiple challenges, such as the complexity of real-world conflict imagery, imbalance between classes, and the inherent ambiguity present in many visual scenes. The NakbaArchiveClassifier shared task introduces a new benchmark for analyzing conflict-related visual data and provides valuable resources for advancing research in humanitarian AI, crisis analytics, and Arabic digital humanities.",{"paper_id":2851,"title":2852,"year":7,"month":358,"day":135,"doi":2853,"resource_url":2854,"first_page":2855,"last_page":2856,"pdf_url":2857,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2858,"paper_type":2663,"authors":2859,"abstract":2870},"lrec2026-ws-nakbanlp-10","The NakbaVirality Shared Task on MultimodalVirality Prediction in High-Stakes Discourse ","10.63317\u002F3qkodh8922a6","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-10","98","103","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.10.pdf","ezzini-etal-2026-nakbavirality",[2860,2861,2864,2865,2868],{"paper_id":2851,"author_seq":459,"given_name":2394,"surname":2395,"affiliation":135,"orcid":135},{"paper_id":2851,"author_seq":434,"given_name":2862,"surname":2863,"affiliation":135,"orcid":135},"Salima","Lamsiyah",{"paper_id":2851,"author_seq":408,"given_name":2283,"surname":2284,"affiliation":135,"orcid":135},{"paper_id":2851,"author_seq":387,"given_name":2866,"surname":2867,"affiliation":135,"orcid":135},"Samir","El-Amrany",{"paper_id":2851,"author_seq":358,"given_name":2674,"surname":2869,"affiliation":135,"orcid":135},"Alsafadi","Social media virality significantly shapes public discourse during geopolitical conflicts, where emotionally charged and multimodal content can rapidly gain widespread attention. However, most prior approaches rely on retrospective engagement signals, limiting their usefulness for early prediction. Multimodal virality modeling in high-stakes Arabic discourse remains largely unexplored. We introduce NakbaVirality, a shared task on multimodal virality classification in conflict-related social media posts, organized as part of the NakbaNLP workshop at LREC 2026. The dataset consists of 2,600 anonymized posts from X and Reddit collected after October 7, 2023, each including text, an associated image, and normalized engagement labels. Participants must classify posts into low, medium, or high virality categories using only textual and visual inputs. The task provides standardized splits, baseline systems, and evaluation using macro-F1 and accuracy. NakbaVirality establishes the first benchmark for multimodal virality prediction in Arabic high-stakes discourse and promotes research on contextual and multimodal modeling for early impact prediction. The shared task attracted 18 participants, who contributed a total of 5 official test phase submissions.",{"paper_id":2872,"title":2873,"year":7,"month":358,"day":135,"doi":2874,"resource_url":2875,"first_page":2876,"last_page":2877,"pdf_url":2878,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2879,"paper_type":2663,"authors":2880,"abstract":2887},"lrec2026-ws-nakbanlp-11","Faisal_Adam at NakbaArchiveClassifier Shared Task: Archival Image Classification for Structural Destruction: A Robust Pipeline Using ResNet-50 and Test-Time Augmentation ","10.63317\u002F3s6qbjzfgsbh","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-11","104","107","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.11.pdf","adam-etal-2026-faisal_adam",[2881,2884],{"paper_id":2872,"author_seq":459,"given_name":2882,"surname":2883,"affiliation":135,"orcid":135},"Faisal Muhammad","Adam",{"paper_id":2872,"author_seq":434,"given_name":2885,"surname":2886,"affiliation":135,"orcid":135},"Salisu","Aliyu","This paper describes our system submission for the Nakba Archive Image Classification task, which requires predicting the presence of structural destruction in historical archival photographs. We framed this as a binary computer vision classification problem (destruction vs. not_destruction). Our system utilizes a pre-trained ResNet-50 convolutional neural network, adapted for binary output, combined with strategic prediction threshold tuning. Evaluated on the unseen final test set, our model achieved a macro F1-score of 0.450 and a balanced accuracy of 0.527, serving as an exploratory baseline that highlights the unique challenges of processing degraded historical imagery.",{"paper_id":2889,"title":2890,"year":7,"month":358,"day":135,"doi":2891,"resource_url":2892,"first_page":2893,"last_page":2894,"pdf_url":2895,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2896,"paper_type":2663,"authors":2897,"abstract":2901},"lrec2026-ws-nakbanlp-12","Doaa Sulaiman at AR-MS NakbaNLP 2026: Faithful Diplomatic Transcription of Arabic Manuscripts Using a Human-Centred Annotation Framework ","10.63317\u002F3ti98qkygayb","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-12","108","112","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.12.pdf","sulaiman-2026-doaa",[2898],{"paper_id":2889,"author_seq":459,"given_name":2899,"surname":2900,"affiliation":135,"orcid":135},"Doaa Bahjat","Sulaiman","This paper describes my participation in the Human Transcription Track (Subtask 1) of the NAKBA-NLP 2026 Arabic Manuscript Understanding Shared Task, which focuses on historical handwritten documents related to Palestinian Nakba narratives. Participant was asked to manually transcribe approximately 500 cropped line images and to design a comprehensive transcription guideline from scratch. I adopted a faithful diplomatic transcription philosophy that preserves original spelling, punctuation, diacritics, and layout features without editorial normalisation, in order to create research-grade gold-standard data. Building on this philosophy, I developed a 26-convention annotation framework organised into three layers: editorial-structural symbols (11 conventions), faithful-copying rules (12 conventions), and documentation labels (3 types), supported by a four-step quality-control pipeline. My submission achieved full coverage of all 500 assigned lines and attained an official Character Error Rate (CER) of 0.02 and accuracy of 0.98, confirming the high precision of the proposed framework.",{"paper_id":2903,"title":2904,"year":7,"month":358,"day":135,"doi":2905,"resource_url":2906,"first_page":2907,"last_page":2908,"pdf_url":2909,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2910,"paper_type":2663,"authors":2911,"abstract":2915},"lrec2026-ws-nakbanlp-13","KvochurHegel at StanceNakba: Robust Stance Detection with Regularized Natural Language Inference ","10.63317\u002F3qqbomcjihz4","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-13","113","117","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.13.pdf","le-2026-kvochurhegel",[2912],{"paper_id":2903,"author_seq":459,"given_name":2913,"surname":2914,"affiliation":135,"orcid":135},"Minh-Hoang","Le","Actor-level stance detection over noisy, politically sensitive data can present challenges that standard training procedures fail to handle reliably. This paper presents KvochurHegel, our submission to the StanceNakba 2026 Shared Task, which addresses these challenges by framing stance classification as Natural Language Inference (NLI) to capture actor-level granularity. The official StanceNakba dataset contains high label noise and topic-correlated spurious features, such as texts discussing unrelated global conflicts using in-domain political vocabulary. To handle these conditions within a three-class schema, we construct templates encoding stance hypotheses for specific actors (e.g., \"The author expresses support for Palestine\") and introduce a broadened neutral class designed to absorb spurious out-of-domain inputs. A DeBERTa-v3 Cross-Encoder independently evaluates the entailment between the input text and each class-specific hypothesis. Because standard cross-entropy training tends to memorize contradictory annotations under these conditions, we regularize the training procedure with R-Drop and label smoothing. This regularized setup likely contributed to robustness against distribution shifts between the competition’s evaluation phases (the public leaderboard and private test set), allowing our model to improve from a Macro-F1 of 0.9094 to 0.9384 without requiring large generative models, cross-validation, or inference-time ensembling.",{"paper_id":2917,"title":2918,"year":7,"month":358,"day":135,"doi":2919,"resource_url":2920,"first_page":2921,"last_page":2922,"pdf_url":2923,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2910,"paper_type":2663,"authors":2924,"abstract":2926},"lrec2026-ws-nakbanlp-14","KvochurHegel at NakbaArchiveClassifier Shared Task: Nakba Image Classification via ConvNeXt-V2 and Label Smoothing ","10.63317\u002F4uecz9j2m37s","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-14","118","120","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.14.pdf",[2925],{"paper_id":2917,"author_seq":459,"given_name":2913,"surname":2914,"affiliation":135,"orcid":135},"This paper presents the KvochurHegel team’s submission to the Nakba Image Classification shared task at the Nakba-NLP 2026 Workshop. The task requires the binary classification of social media images into destruction and not_destruction categories. Given a limited and imbalanced training set of 1,400 images, we utilized a ConvNeXt-V2 Nano backbone combined with extensive data augmentation and label smoothing, prioritizing standard regularization over task-specific architectural modifications. For inference, we applied a 6-view Test-Time Augmentation (TTA) strategy using a hard-voting mechanism. The baseline system achieved a Macro F1-score of 0.8593 and an Accuracy of 0.8706 on the official private test set, ranking 6th out of 16 participating teams.",{"paper_id":2928,"title":2929,"year":7,"month":358,"day":135,"doi":2930,"resource_url":2931,"first_page":2932,"last_page":2933,"pdf_url":2934,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2935,"paper_type":2663,"authors":2936,"abstract":2952},"lrec2026-ws-nakbanlp-15","HCMUS_TheFangs at NakbaArchiveClassifier Shared Task: Foundation Models and Advanced Training Strategies for Conflict Damage Classification ","10.63317\u002F26tv58oaqfc4","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-15","121","127","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.15.pdf","daosy-etal-2026-hcmus_thefangs",[2937,2940,2943,2946,2949],{"paper_id":2928,"author_seq":459,"given_name":2938,"surname":2939,"affiliation":135,"orcid":135},"Duy Minh","Dao Sy",{"paper_id":2928,"author_seq":434,"given_name":2941,"surname":2942,"affiliation":135,"orcid":135},"Trung Kiet","Huynh",{"paper_id":2928,"author_seq":408,"given_name":2944,"surname":2945,"affiliation":135,"orcid":135},"Nguyen Chi","Tran",{"paper_id":2928,"author_seq":387,"given_name":2947,"surname":2948,"affiliation":135,"orcid":135},"Phu Quy","Nguyen Lam",{"paper_id":2928,"author_seq":358,"given_name":2950,"surname":2951,"affiliation":135,"orcid":135},"Phu Hoa","Pham","We present our system for the NakbaArchiveClassifier shared task at Nakba-NLP 2026, which requires classifying Instagram images from Gaza as showing destroyed or damaged infrastructure versus intact surroundings. Working with a small, imbalanced dataset (1,400 training images; 1.83:1 class ratio), we conduct a systematic empirical study of six model-training combinations spanning five architecture families: standard CNNs (EfficientNet-B4), self-supervised ViTs (DINOv2-ViT-L), hybrid multi-axis Transformers (MaxViT-Base), masked-image-modelling ViTs (EVA-02-Base), and large-kernel CNNs (UniRepLKNet). For our best performing configuration–MaxViT-Base with focal loss, MixUp, and a rich geometric augmentation pipeline–we provide a detailed component analysis. Our system achieves a macro F1 of 0.899 on the public test set, ranking 1st on the competition leaderboard. We additionally report findings from novel experiments including a Kolmogorov-Arnold Network (KAN) classification head and VLM-regularized training with BLIP-2-generated captions, offering insights into what does and does not transfer to conflict-domain imagery under severe data scarcity.",{"paper_id":2954,"title":2955,"year":7,"month":358,"day":135,"doi":2956,"resource_url":2957,"first_page":2958,"last_page":2959,"pdf_url":2960,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2961,"paper_type":2663,"authors":2962,"abstract":2969},"lrec2026-ws-nakbanlp-16","Baflah-lamri at NAKBA-NLP 2026: Manual Ground Truth Enrichment ","10.63317\u002F4aymwtp2ujku","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-16","128","132","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.16.pdf","abdelouahab-etal-2026-baflah",[2963,2966],{"paper_id":2954,"author_seq":459,"given_name":2964,"surname":2965,"affiliation":135,"orcid":135},"baflah","abdelouahab",{"paper_id":2954,"author_seq":434,"given_name":2967,"surname":2968,"affiliation":135,"orcid":135},"Lamri","Mohamed","This paper presents a detailed description of the team’s methodology methodology in participating in Subtask 1 (Transcription Track) of the NAKBA NLP 2026 Shared Task for Arabic Manuscript Understanding. We present a rigorous approach to line-level manual transcription of historical Arabic manuscripts derived from the Omar Al-Saleh memoir collection (1951-1965). Our methodology emphatisez accuracy, consistency, and adherence to diplomatic transcription principles, while addressing the unique palaeographic and physical challenges of Arabic handwriting, such as writing speed, orthographic variation, and the impact of writing tools (e.g., immediate strike-throughs and ink spatter). The work guided by strict transcription guidelines and contextual verification protocols, matching cropped line images with full-page images to resolve ambiguities and automated cropping issues. The team successfully transcribed the entire assigned batch of 500 lines (100% completion rate) across 368 unique pages, producing reference data comprising 6,719 words and 37,646 characters. This effort contributes to providing highly reliable Ground Truth data, serving as an essential foundation for training and evaluating Handwritten Text Recognition (HTR) models for Arabic manuscripts.",{"paper_id":2971,"title":2972,"year":7,"month":358,"day":135,"doi":2973,"resource_url":2974,"first_page":2975,"last_page":2976,"pdf_url":2977,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2978,"paper_type":2663,"authors":2979,"abstract":2985},"lrec2026-ws-nakbanlp-17","Free-Gaza at NakbaArchiveClassifier Shared Task: Towards Distinguishing the Destructive Effect of Nakba: NakbaImage Classification Using Artificial Intelligence Techniques ","10.63317\u002F59i3kmpytngj","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-17","133","136","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.17.pdf","yassin-etal-2026-free",[2980,2983],{"paper_id":2971,"author_seq":459,"given_name":2981,"surname":2982,"affiliation":135,"orcid":135},"Nisreen I. R.","Yassin",{"paper_id":2971,"author_seq":434,"given_name":2984,"surname":2290,"affiliation":135,"orcid":135},"Enas A. Hakim","The accounts of the continuing Palestinian Nakba encompass considerable significance. Over the course of the three years of the conflict, millions of photos from social media have been preserved. The preservation and classification of these data through artificial intelligence tools are essential to guarantee their availability, accessibility, and applicability. This paper presents a highly optimized, resource-constrained machine learning pipeline for binary image classification. The system is designed for the NakbaArchiveClassifier Shared Task 2026, which aims to distinguish between destroyed infrastructural images and intact infrastructural images. The system depends on two lightweight EfficientNetB0 networks to build a weighted ensemble system. Using strict hardware limitations of 2GB GPU VRAM, the system achieves an F1-score of 84.16%, which ranked 9th on the leaderboard.",{"paper_id":2987,"title":2988,"year":7,"month":358,"day":135,"doi":2989,"resource_url":2990,"first_page":2991,"last_page":2992,"pdf_url":2993,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":2994,"paper_type":2663,"authors":2995,"abstract":2997},"lrec2026-ws-nakbanlp-18","HCMUS_TheFangs at NakbaVirality Shared Task: The Audience is the Message: Escaping the Deep Learning Trap in Conflict-Domain Virality Prediction ","10.63317\u002F3bdi84ncsp38","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-18","137","143","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.18.pdf","daosy-2026-hcmus_thefangs",[2996],{"paper_id":2987,"author_seq":459,"given_name":2938,"surname":2939,"affiliation":135,"orcid":135},"We present HCMUS_TheFangs’s system for the Nakba-NLP 2026 Virality Shared Task, which achieves Rank #1 on the final leaderboard with a test Macro-F1 of 0.7062, placing first among all competing teams. Our winning system is deliberately simple: a single Community Target Encoding feature - the smoothed historical virality rate of the posting subreddit - combined with TF-IDF text features and an XGBoost classifier. This design emerged from a hard-won insight: virality in conflict reporting is determined not by what is posted but by where it is posted. We spend the majority of this paper showing why this holds. Through 18 ablation experiments we trace the journey from deep learning failure (a cross-attention fusion model scoring 0.2935) to sociological feature engineering. We demonstrate that in conflict domains, deep learning overfits, promotional hashtags negatively correlate with engagement, and visual features are context-dependent modifiers rather than independent signals. Our findings challenge standard practices in multimodal classification and offer a roadmap for predicting virality in highly polarized, community-driven social media environments.",{"paper_id":2999,"title":3000,"year":7,"month":358,"day":135,"doi":3001,"resource_url":3002,"first_page":3003,"last_page":3004,"pdf_url":3005,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3006,"paper_type":2663,"authors":3007,"abstract":3014},"lrec2026-ws-nakbanlp-19","Xin1212 at NakbaVirality Shared Task: Frozen CLIP with Residual Adapter for Multimodal Virality Classification ","10.63317\u002F3myy3h8imvfz","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-19","144","146","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.19.pdf","zhang-etal-2026-xin1212",[3008,3011],{"paper_id":2999,"author_seq":459,"given_name":3009,"surname":3010,"affiliation":135,"orcid":135},"Xinyan","Zhang",{"paper_id":2999,"author_seq":434,"given_name":3012,"surname":3013,"affiliation":135,"orcid":135},"Bingzhou","Yang","We describe our system for the NakbaVirality shared task on multimodal virality classification. Our final approach uses a frozen LAION CLIP backbone, a lightweight residual adapter over fused text–image embeddings, and a small MLP classification head. On our development split, the best configuration (V9) achieves Macro-F1 of 0.5492 and virality-weighted F1 of 0.5252. On the official test submission, our system obtains F1-score 0.4559 and accuracy 0.6089 according to the platform scorer. We provide implementation details, ablations across multiple versions (Baseline–V9), and practical error analysis for reproducibility.",{"paper_id":3016,"title":3017,"year":7,"month":358,"day":135,"doi":3018,"resource_url":3019,"first_page":3020,"last_page":3021,"pdf_url":3022,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3023,"paper_type":2663,"authors":3024,"abstract":3030},"lrec2026-ws-nakbanlp-20","A2NLP at StanceNakba Shared Task: Fine-Tuned AraBERT for Topic-Based Arabic Stance Detection ","10.63317\u002F5jy5nrvfrzzk","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-20","147","159","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.20.pdf","nairat-etal-2026-a2nlp",[3025,3028],{"paper_id":3016,"author_seq":459,"given_name":3026,"surname":3027,"affiliation":135,"orcid":135},"Alaa","Nairat",{"paper_id":3016,"author_seq":434,"given_name":3029,"surname":3027,"affiliation":135,"orcid":135},"Aysar Mahmoud","AbstractThis paper describes A2NLP’s system for Subtask B of the StanceNakba Shared Task, which addresses cross-topic Arabic stance detection. The goal is to classify sentence–topic pairs into pro, against, or neutral labels. We introduce a topic-conditioned prompting strategy built on AraBERTv0.2-Twitter, where each instance is reformulated into a structured prompt that explicitly models the interaction between the sentence and its target topic. The model is trained using 5-fold stratified cross-validation with class-weighted loss to ensure robustness under mild label imbalance. Our final submission achieves a Macro-F1 score of 0.8483 on the official test set, outperforming the AraBERTv2 baseline (0.810) and ranking fifth overall. Ablation analysis confirms that topic-conditioned prompting substantially improves generalization across topics. The findings demonstrate the importance of structured input design and domain-aligned pretraining for reliable stance detection in dialectal Arabic social media discourse.",{"paper_id":3032,"title":3033,"year":7,"month":358,"day":135,"doi":3034,"resource_url":3035,"first_page":3036,"last_page":3037,"pdf_url":3038,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3039,"paper_type":2663,"authors":3040,"abstract":3053},"lrec2026-ws-nakbanlp-21","Ketaba-OCR at AR-MS NakbaNLP 2026: Efficient Adaptation of Vision-Language Models for Handwritten Recognition ","10.63317\u002F3inc2znes52o","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-21","160","170","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.21.pdf","barmandah-etal-2026-ketaba",[3041,3044,3047,3050],{"paper_id":3032,"author_seq":459,"given_name":3042,"surname":3043,"affiliation":135,"orcid":135},"Hassan","Barmandah",{"paper_id":3032,"author_seq":434,"given_name":3045,"surname":3046,"affiliation":135,"orcid":135},"Fatimah Emad","Eldin",{"paper_id":3032,"author_seq":408,"given_name":3048,"surname":3049,"affiliation":135,"orcid":135},"Khloud","Al Jallad",{"paper_id":3032,"author_seq":387,"given_name":3051,"surname":3052,"affiliation":135,"orcid":135},"Omer","Nacar","This paper presents Ketaba-OCR-LoRA, a system developed for the NakbaNLP 2026 Shared Task on Arabic Manuscript Understanding (Subtask 2), which targets the transcription of the historically significant Omar Al-Saleh Memoir Collection written in Ruq’ah and Naskh scripts. We propose a parameter-efficient adaptation of a publicly available pretrained Arabic-English Handwritten Text Recognition (HRT) model, originally trained on handwritten corpora including the Muharaf dataset. Instead of adapting general Vision-Language Models from scratch, we fine-tune the HRT backbone using Low-Rank Adaptation (LoRA) and 4-bit quantization (QLoRA), reducing memory requirements from 40GB to approximately 8GB. Our final submission combines multiple model variants through a novel Linear+Boost weighted ensemble strategy. Our approach achieves a CER of 0.0819 and WER of 0.2588 on the blind test set (per-line evaluation), ranking 1st on per-line evaluation; on the official corpus-wide leaderboard, we rank 3rd (CER 0.0938, WER 0.2996). This work demonstrates that specialized pretrained HRT models substantially outperform general-purpose Vision-Language Models for Arabic manuscript transcription, and that parameter-efficient fine-tuning provides a practical and reproducible approach for low-resource cultural heritage digitization.",{"paper_id":3055,"title":3056,"year":7,"month":358,"day":135,"doi":3057,"resource_url":3058,"first_page":3059,"last_page":3060,"pdf_url":3061,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3062,"paper_type":2663,"authors":3063,"abstract":3071},"lrec2026-ws-nakbanlp-22","No Overfit at NakbaArchiveClassifier Shared Task: A Swin Transformer-Based System for Destruction Image Classification ","10.63317\u002F28o7zedpovh7","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-22","171","176","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.22.pdf","mohamed-etal-2026-no",[3064,3066,3069],{"paper_id":3055,"author_seq":459,"given_name":3065,"surname":2968,"affiliation":135,"orcid":135},"Mohamed Fathy",{"paper_id":3055,"author_seq":434,"given_name":3067,"surname":3068,"affiliation":135,"orcid":135},"Samar Mahmoud","Abd El-Mageed",{"paper_id":3055,"author_seq":408,"given_name":3070,"surname":2968,"affiliation":135,"orcid":135},"Ensaf","Automated destruction identification from visual data plays a critical role in large-scale documentation, humanitarian analysis, and digital archiving of conflict-related events. Within this context, the Nakba-NLP 2026 Workshop introduced a shared task aimed at training and evaluating a binary image classification model to distinguish between destroyed or damaged infrastructure and intact infrastructure. However, the limited dataset size and the visual variability of real-world scenes make this task particularly challenging. This work presents a Swin Transformer–based framework tailored for destruction image classification. The proposed model employs a hierarchical Swin Transformer backbone for robust feature extraction, followed by a multi-layer perceptron classifier for decision-making. To address the limited data issue, transfer learning and a customized training strategy are applied to adapt the model effectively without full end-to-end retraining. Furthermore, a semi-supervised data expansion approach is utilized to enlarge the training set from 1,400 to 10,000 images, improving model generalization and robustness. Experimental results on the official blind test set demonstrate strong performance, achieving an F1-score of 86.55% and an accuracy of 87.81%, ranking 5th in the shared task.",{"paper_id":3073,"title":3074,"year":7,"month":358,"day":135,"doi":3075,"resource_url":3076,"first_page":3077,"last_page":3078,"pdf_url":3079,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3080,"paper_type":2663,"authors":3081,"abstract":3091},"lrec2026-ws-nakbanlp-23","Yafa at StanceNakba: Actor-Level Stance Detection Using Cross-Lingual Approach ","10.63317\u002F4pzmawd3avoy","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-23","177","181","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.23.pdf","zayet-etal-2026-yafa",[3082,3085,3088],{"paper_id":3073,"author_seq":459,"given_name":3083,"surname":3084,"affiliation":135,"orcid":135},"Tasnim","Zayet",{"paper_id":3073,"author_seq":434,"given_name":3086,"surname":3087,"affiliation":135,"orcid":135},"Osama","Hamed",{"paper_id":3073,"author_seq":408,"given_name":3089,"surname":3090,"affiliation":135,"orcid":135},"Tasneem","Duridi","This paper addresses the problem of actor-level stance detection in English social media posts concerning the Palestinian issue, a subtask of the StanceNakba-2026 Shared Task. The objective is to classify posts into one of three categories: Pro-Palestine, Pro-Israel, or Neutral, which is more challenging than the traditional favor\u002Fagainst\u002Fneutral formulations. This study uses a dataset comprising 1,401 posts, collected from X (formerly Twitter) after October 7, 2023, and annotated with one of the three stance labels. As Yafa’s Team, we tried to solve this problem using BERT-based models, which have proven their superiority in similar tasks. Several BERT-based models were fine-tuned and compared, including ARBERT, MARBERT, and PoliBERTweet, among others. Our winning model is the \"MARBERT-Y\", where the \"Y\" comes from Yafa, a MARBERT-based model that has achieved a macro-F1 score of 95% on the test set. We argue this to two main factors: the structured and harsh preprocessing steps applied and the fine-tuning process employed. This indicates that domain-adapted transformer models, i.e., those pretrained on large-scale Twitter data are highly effective for politically stance detection tasks.",{"paper_id":3093,"title":3094,"year":7,"month":358,"day":135,"doi":3095,"resource_url":3096,"first_page":3097,"last_page":3098,"pdf_url":3099,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3100,"paper_type":2663,"authors":3101,"abstract":3105},"lrec2026-ws-nakbanlp-24","The Resistant Word at StanceNakba Shared Task: A Topic-Aware Model for Cross-Topic Stance Detection ","10.63317\u002F33r5jdoe6avj","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-24","182","186","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.24.pdf","yassine-2026-resistant",[3102],{"paper_id":3093,"author_seq":459,"given_name":3103,"surname":3104,"affiliation":135,"orcid":135},"Sarah","Yassine","Cross-topic stance detection in Arabic is the task of identifying whether a text expresses a pro, against, or neutral position toward a given issue, and it is particularly challenging under topic shifts and class imbalance. In Subtask B of the StanceNakba 2026 shared task on Arabic cross-topic stance detection, we are given a Levantine Arabic sentence and one of two topics: \"Normalization with Israel\" or \"Refugee\u002FImmigrant Presence in Jordan,\" and we must classify the expressed stance. A central difficulty is the systematic failure of standard fine-tuning to recognize the minority neutral class, driven by majority-class dominance in cross-entropy training and accuracy-based checkpoint selection. To address this, we combine random oversampling with class-weighted cross-entropy loss, and we build an ensemble of four Arabic pre-trained transformers MARBERT, AraBERT Large, XLM-RoBERTa Base, and CAMeL-BERT Mix each trained using Stratified 5-Fold cross-validation. Our final system achieves a macro-F1 of 0.9777 and an accuracy of 97.79% on the evaluation set.",{"paper_id":3107,"title":3108,"year":7,"month":358,"day":135,"doi":3109,"resource_url":3110,"first_page":3111,"last_page":3112,"pdf_url":3113,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3114,"paper_type":2663,"authors":3115,"abstract":3122},"lrec2026-ws-nakbanlp-25","Hope at NakbaArchiveClassifier Shared Task: Transfer Learning-Based CNN Models for Infrastructure Damage Detection ","10.63317\u002F233j9kgmifw4","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-25","187","190","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.25.pdf","alkhidir-etal-2026-hope",[3116,3119],{"paper_id":3107,"author_seq":459,"given_name":3117,"surname":3118,"affiliation":135,"orcid":135},"Lojien","AlKhidir",{"paper_id":3107,"author_seq":434,"given_name":3120,"surname":3121,"affiliation":135,"orcid":135},"HebaTalla","Abdelhady","This paper describes Team Hope’s system for the NakbaArchiveClassifier Shared Task at Nakba-NLP 2026. The task focuses on binary classification of social media images into two categories: destruction and not_destruction. We evaluated multiple convolutional neural network architectures using transfer learning, including ResNet34, ResNet50, EfficientNet-B0, and a fine-tuned ResNet34 variant with staged training. All models were initialized with ImageNet pretrained weights and fine-tuned on the provided dataset of 2,001 images. The dataset is moderately imbalanced and contains visually diverse Instagram images depicting intact and damaged infrastructure. Our best-performing model, ResNet34 trained for 25 epochs with Adam optimizer and a learning rate of 1e-4, achieved 81% accuracy on the evaluation platform. We provide a comparative analysis of the tested architectures and discuss the impact of model depth, training duration, and class imbalance. Given the political and ethical sensitivity of the dataset, we also include a discussion of responsible AI considerations and potential limitations. Our findings suggest that moderate-depth architectures can generalize effectively in low-resource, contextually complex visual classification tasks.",{"paper_id":3124,"title":3125,"year":7,"month":358,"day":135,"doi":3126,"resource_url":3127,"first_page":3128,"last_page":3129,"pdf_url":3130,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3131,"paper_type":2663,"authors":3132,"abstract":3151},"lrec2026-ws-nakbanlp-26","Al-Warraq at AR-MS NAKBA-NLP 2026: Adapting Vision-Language and Transformer Models for Automatic Manuscript OCR\u002FHTR ","10.63317\u002F2fv6385csb36","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-26","191","195","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.26.pdf","youssef-etal-2026-al",[3133,3136,3139,3142,3145,3148],{"paper_id":3124,"author_seq":459,"given_name":3134,"surname":3135,"affiliation":135,"orcid":135},"Ahmad Edris","Youssef",{"paper_id":3124,"author_seq":434,"given_name":3137,"surname":3138,"affiliation":135,"orcid":135},"Aya Hafiz","Faris",{"paper_id":3124,"author_seq":408,"given_name":3140,"surname":3141,"affiliation":135,"orcid":135},"Alhasan","Hamood",{"paper_id":3124,"author_seq":387,"given_name":3143,"surname":3144,"affiliation":135,"orcid":135},"Zainab","Kamil",{"paper_id":3124,"author_seq":358,"given_name":3146,"surname":3147,"affiliation":135,"orcid":135},"Jana","Alqasem",{"paper_id":3124,"author_seq":333,"given_name":3149,"surname":3150,"affiliation":135,"orcid":135},"SARA Ali","hamed\"","We present our submission to the NAKBA NLP 2026 Automatic Manuscript OCR\u002FHTR shared task on Arabic manuscripts. The task aims to transcribe manuscript line images into machine-readable Arabic text. Our approach followed an iterative pipeline including model selection, training, error analysis, test-time augmentation, and postprocessing. After evaluating several OCR\u002FHTR models, we selected and trained the most suitable model on the provided manuscript line images and transcriptions. Error analysis showed better character-level performance than word-level performance, which motivated the use of test-time augmentation and text cleaning to improve robustness. The final system achieved a CER of 0.1142 and a WER of 0.378, placing fifth in the shared task. These results show that simple but targeted improvements can support effective Arabic manuscript transcription.",{"paper_id":3153,"title":3154,"year":7,"month":358,"day":135,"doi":3155,"resource_url":3156,"first_page":3157,"last_page":3158,"pdf_url":3159,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3160,"paper_type":2663,"authors":3161,"abstract":3174},"lrec2026-ws-nakbanlp-27","Misraj AI at AR-MS NAKBA-NLP 2026: A State-of-the-Art VLM in Arabic Handwritten Text Recognition ","10.63317\u002F2uur73vrcmk5","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-27","196","200","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.27.pdf","hennara-etal-2026-misraj",[3162,3164,3166,3169,3171],{"paper_id":3153,"author_seq":459,"given_name":2290,"surname":3163,"affiliation":135,"orcid":135},"Hennara",{"paper_id":3153,"author_seq":434,"given_name":2328,"surname":3165,"affiliation":135,"orcid":135},"Hreden",{"paper_id":3153,"author_seq":408,"given_name":3167,"surname":3168,"affiliation":135,"orcid":135},"Zeina","Aldallal",{"paper_id":3153,"author_seq":387,"given_name":107,"surname":3170,"affiliation":135,"orcid":135},"Chrouf",{"paper_id":3153,"author_seq":358,"given_name":3172,"surname":3173,"affiliation":135,"orcid":135},"Safwan","AlModhayan","Handwritten Text Recognition (HTR) for Arabic presents unique challenges due to the script’s cursive nature, varying writer styles, and morphological complexity. While modern Vision-Language Models (VLMs) have significantly advanced document parsing, their direct application to highly specific cursive domains requires strategic adaptation. This paper details our submission to the Nakba OCR competition, which adapts a 3B-parameter VLM to recognize historical Arabic manuscripts. We employ a progressive training pipeline that utilizes domain-matched data augmentation to bridge the gap between standard printed Arabic OCR and historical handwritten manuscripts. Moving beyond standard decoder-only Supervised Fine-Tuning (SFT), we fine-tune the entire encoder-decoder architecture using differential learning rates. This approach, followed by a final checkpoint merge, allows the model to better resolve the fine visual details of cursive Arabic script. Our final unified model (submitted under the team name Misraj AI) establishes a new state-of-the-art (SOTA) on the Nakba dataset, achieving a Word Er- ror Rate (WER) of 0.24 and a Character Error Rate (CER) of 0.08, and officially securing first place on the leaderboard.",{"paper_id":3176,"title":3177,"year":7,"month":358,"day":135,"doi":3178,"resource_url":3179,"first_page":3180,"last_page":3181,"pdf_url":3182,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3183,"paper_type":2663,"authors":3184,"abstract":3194},"lrec2026-ws-nakbanlp-28","PushingBoundaries at NakbaVirality Shared Task: Recursive Prompt Improvement for Multimodal Virality Classification ","10.63317\u002F2bhypjjhwbsa","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-28","201","205","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.28.pdf","islam-etal-2026-pushingboundaries",[3185,3188,3190,3193],{"paper_id":3176,"author_seq":459,"given_name":3186,"surname":3187,"affiliation":135,"orcid":135},"Ashhadul","Islam",{"paper_id":3176,"author_seq":434,"given_name":3189,"surname":2824,"affiliation":135,"orcid":135},"Md Rafiul",{"paper_id":3176,"author_seq":408,"given_name":3191,"surname":3192,"affiliation":135,"orcid":135},"Samir Brahim","Belhaouari",{"paper_id":3176,"author_seq":387,"given_name":2704,"surname":2705,"affiliation":135,"orcid":135},"This paper describes our participation in the NakbaVirality shared task at the NakbaNLP Workshop (LREC–COLING 2026). We investigate Recursive Prompt Improvement (RPI), an instruction-level optimization strategy for virality classification in high-stakes geopolitical discourse. In this work, we propose a self-supervised approach to iteratively improve the classification prompt without human intervention. We begin with a basic prompt that guides the LLM to perform multi-class classification, incorporating contextual information about the tweets. After obtaining predictions, we identify misclassified tweets and feed them back to the model with an instruction to refine and improve the original classification prompt. This process is repeated over multiple iterations to assess whether performance improves over time. Our results show a remarkable improvement in F1 score from the first iteration to the final one. Although the proposed method does not reach the accuracy of models fine-tuned directly on task-specific data, it demonstrates that iterative, self-supervised prompt refinement can serve as a viable proxy for fine-tuning. By leveraging the model’s own errors as feedback, this approach reduces reliance on computationally expensive training procedures and heavy GPU usage, while preserving much of the adaptability typically associated with fine-tuned models. This paradigm opens promising avenues for resource-efficient model adaptation and suggests new directions for scalable, low-cost performance improvement without traditional fine-tuning. The code has been shared in Github.",{"paper_id":3196,"title":3197,"year":7,"month":358,"day":135,"doi":3198,"resource_url":3199,"first_page":3200,"last_page":3201,"pdf_url":3202,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3203,"paper_type":2663,"authors":3204,"abstract":3214},"lrec2026-ws-nakbanlp-29","U4RASD at StanceNakba Shared Task: Data Augmentation and Auxiliary Objectives for Arabic Stance Detection ","10.63317\u002F2dsxtdvvu8vu","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-29","206","211","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.29.pdf","hamdan-etal-2026-u4rasd",[3205,3207,3210,3212],{"paper_id":3196,"author_seq":459,"given_name":25,"surname":3206,"affiliation":135,"orcid":135},"Hamdan",{"paper_id":3196,"author_seq":434,"given_name":3208,"surname":3209,"affiliation":135,"orcid":135},"Aya","Jouni",{"paper_id":3196,"author_seq":408,"given_name":3208,"surname":3211,"affiliation":135,"orcid":135},"Saïd",{"paper_id":3196,"author_seq":387,"given_name":3213,"surname":2807,"affiliation":135,"orcid":135},"Fadi","This paper describes a submission to Track B of the StanceNakba Shared Task on Arabic cross-topic stance detection in the political domain. We investigate LLM-based data augmentation, auxiliary training objectives including contrastive and multi-task learning, zero-shot prompting, and a preliminary terminology-based clustering approach. Our final system, based on MARBERTv2 with dialect-aware LLM-based augmentation, achieved 86% macro-F1 on the blind test set and ranked 3rd out of 10 teams. Our results show that dialect-aware augmentation substantially improved performance in a low-resource Arabic stance detection setting, while not all auxiliary objectives or clustering-based strategies yielded consistent gains. We release our code at https:\u002F\u002Facr.ps\u002F1L9B9Tw.",{"paper_id":3216,"title":3217,"year":7,"month":358,"day":135,"doi":3218,"resource_url":3219,"first_page":3220,"last_page":3221,"pdf_url":3222,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3223,"paper_type":2663,"authors":3224,"abstract":3230},"lrec2026-ws-nakbanlp-30","AyahVerse at NakbaArchiveClassifier Shared Task: Architectural Trade-offs and Decision Calibration for Humanitarian Image Classification ","10.63317\u002F3p7n5t5hmz6h","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-30","212","216","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.30.pdf","rashid-etal-2026-ayahverse",[3225,3228],{"paper_id":3216,"author_seq":459,"given_name":3226,"surname":3227,"affiliation":135,"orcid":135},"Ibad-ur-Rehman","Rashid",{"paper_id":3216,"author_seq":434,"given_name":3229,"surname":490,"affiliation":135,"orcid":135},"Akhtar","This paper presents our submission to the Nakba-NLP 2026 Shared Task on binary image classification, where the goal is to categorize images of Gaza infrastructure as destroyed or intact. To address the challenges of class imbalance and resource-constrained deployment, we evaluated three convolutional architectures: ResNet50, MobileNetV2, and EfficientNet-B0, combined with a post-hoc threshold optimization step. Our results show that lightweight architectures are competitive with heavier models for this task, with EfficientNet-B0 achieving the highest Test F1-score of 0.85 despite having significantly fewer parameters than ResNet50. We further investigated the effect of input resolution, finding that increasing resolution improved ResNet50’s performance, though it remained below lightweight alternatives. Finally, we demonstrate that shifting the binary decision threshold from the default 0.50 to an optimized 0.45 improved ResNet50’s Test F1 from 0.79 to 0.81 by recovering recall for the minority destroyed class. Notably, this adjustment was only needed for ResNet50, while EfficientNet-B0 and MobileNetV2 performed best at the default 0.50, suggesting that larger models are more prone to majority-class bias. Overall, these results provide a systematic analysis of architectural efficiency and threshold behavior under class imbalance, offering practical insights for damage classification in resource-constrained crisis settings.",{"paper_id":3232,"title":3233,"year":7,"month":358,"day":135,"doi":3234,"resource_url":3235,"first_page":3236,"last_page":3237,"pdf_url":3238,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3239,"paper_type":2663,"authors":3240,"abstract":3244},"lrec2026-ws-nakbanlp-31","mlenthusiast at NakbaArchiveClassifier Shared Task: A Lightweight SVM-Gated Ensemble of EfficientNets for Image Classification ","10.63317\u002F5aw7xdt47vzs","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-31","217","220","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.31.pdf","hossain-2026-mlenthusiast",[3241],{"paper_id":3232,"author_seq":459,"given_name":3242,"surname":3243,"affiliation":135,"orcid":135},"Md. Ajwad","Hossain","Image classification under strict time constraints requires a delicate balance between feature complexity and computational overhead. This paper presents an optimized ensemble methodology developed for the NAKABA competition, focusing on identifying structural destruction. We propose a hybrid architecture that leverages two distinct Convolutional Neural Networks (EfficientNetB0 and EfficientNetB3) as base feature extractors, coupled with a Support Vector Machine (SVM) functioning as a meta-classifier. Instead of standard probability averaging or processing high-dimensional embeddings directly, the Meta-SVM acts as a learned gating mechanism to optimally combine the low-dimensional probability predictions of the base models. This ensures robust performance without the latency of heavier deep learning architectures. Empirical results demonstrate the efficacy of this approach. The model achieved a validation accuracy of 0.884 and a weighted F1-score of 0.885, with a notable F1-score of 0.839 on the challenging ’destruction’ class. On the official NAKABA leaderboard test set, the ensemble maintained strong generalization, achieving an F1-score of 0.831 and an accuracy of 0.845, which secured the 12th position overall and proved the model’s high effectiveness within the competition’s strict operational constraints.",{"paper_id":3246,"title":3247,"year":7,"month":358,"day":135,"doi":3248,"resource_url":3249,"first_page":3250,"last_page":3251,"pdf_url":3252,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3253,"paper_type":2663,"authors":3254,"abstract":3258},"lrec2026-ws-nakbanlp-32","Pixel at NakbaArchiveClassifier Shared Task: ConvNeXt-Based Ensemble for Destruction Detection ","10.63317\u002F3o45xv3zt4nu","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-32","221","225","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.32.pdf","jaber-2026-pixel",[3255],{"paper_id":3246,"author_seq":459,"given_name":3256,"surname":3257,"affiliation":135,"orcid":135},"Rahaf","Jaber","This paper describes our submission to the Nakba Image Classification Shared Task at the Nakba-NLP 2026 workshop. The task requires binary classification of social media images into two categories: destruction and not_destruction. The dataset includes approximately 1,600 annotated development images and 400 held-out test images, collected from Instagram posts published in Gaza between October 2023 and December 2025. High variability in viewpoint, lighting, and image quality, coupled with the inherent complexities of identifying structural damage in dense urban environments, makes this task particularly challenging. Our system utilizes a pretrained ConvNeXt-Tiny backbone fine-tuned through a stratified 5-fold cross-validation framework. To mitigate class imbalance, we implement a weighted cross-entropy loss function. During the inference phase, we employ an ensemble strategy that averages predictions across all five fold-specific models, and test-time augmentation (TTA) is applied to enhance robustness. The final ensemble achieved a Macro F1-score of 0.8952 and an accuracy of 0.9055 on the official test set. Our results suggest that the integration of modern convolutional architectures with robust ensembling and augmentation strategies provides a reliable baseline for automated destruction detection.",{"paper_id":3260,"title":3261,"year":7,"month":358,"day":135,"doi":3262,"resource_url":3263,"first_page":3264,"last_page":3265,"pdf_url":3266,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3267,"paper_type":2663,"authors":3268,"abstract":3272},"lrec2026-ws-nakbanlp-33","MennaAly at NakbaArchiveClassifier Shared Task: Transfer Learning with ResNet for Historical Image Classification ","10.63317\u002F26ohd3yrshim","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-33","226","228","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.33.pdf","aly-2026-mennaaly",[3269],{"paper_id":3260,"author_seq":459,"given_name":3270,"surname":3271,"affiliation":135,"orcid":135},"menna","aly","This paper describes our submission to the NakbaArchiveClassifier shared task at Nakba-NLP 2026, co-located with LREC 2026. The task consists of binary image classification, where a model must classify historical images into one of two categories: destruction or not_destruction. We adopt a transfer learning approach based on pretrained residual networks, fine-tuned on the provided training data. To mitigate class imbalance, we incorporate weighted cross-entropy loss during optimization. In the development phase, our ResNet18 model achieved a peak macro F1-score of 0.8137 on the validation set. For the final phase, we trained on the combined training and validation data (1,599 labeled images) and generated predictions for the hidden test set of 402 images. Our final submission achieved a macro F1-score of 0.83228 with an accuracy of 0.84577 on the official evaluation set. These results underscore the effectiveness of lightweight transfer learning approaches for historical image analysis under limited-data conditions, demonstrating that compact residual architectures can achieve competitive performance without complex architectural modifications.",{"paper_id":3274,"title":3275,"year":7,"month":358,"day":135,"doi":3276,"resource_url":3277,"first_page":3278,"last_page":3279,"pdf_url":3280,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3281,"paper_type":2663,"authors":3282,"abstract":3289},"lrec2026-ws-nakbanlp-34","DLRG@ NakbaArchiveClassifier Shared Task: Deep Transfer Learning for Destruction Detection in Nakba Archive Images Using EfficientNet-B3 ","10.63317\u002F4uzdggnax4e2","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-34","229","233","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.34.pdf","kannan-etal-2026-dlrg",[3283,3286],{"paper_id":3274,"author_seq":459,"given_name":3284,"surname":3285,"affiliation":135,"orcid":135},"Ramesh R.","Kannan",{"paper_id":3274,"author_seq":434,"given_name":3287,"surname":3288,"affiliation":135,"orcid":135},"Ratnavel","Rajalakshmi","Automatic identification of destruction in conflict-affected regions is an important task for humanitarian monitoring and historical documentation. Visual analysis of destruction scenes can assist researchers and policy makers in understanding the extent of damage in affected areas. This paper presents a deep learning-based image classification approach for identifying destruction and non-destruction scenes in Nakba-related images. The problem is formulated as binary image classification on Nakba images. A transfer learning approach using EfficientNet-B3 is adopted to learn discriminative visual features from Nakba images. Experimental evaluation shows that the proposed model achieved an Weighted F1-score of 83.87 % and an overall classification accuracy of 85.57 % and secured 10th rank in the competition. The results demonstrate that our proposed pre-trained method can effectively capture structural damage patterns and visual cues associated with destruction scenes. Code: https:\u002F\u002Fgithub.com\u002Fkannanrrk\u002FNakbaImageClassifier",{"paper_id":3291,"title":3292,"year":7,"month":358,"day":135,"doi":3293,"resource_url":3294,"first_page":3295,"last_page":3296,"pdf_url":3297,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3298,"paper_type":2663,"authors":3299,"abstract":3308},"lrec2026-ws-nakbanlp-35","Oblevit at AR-MS NAKBA NLP 2026 Subtask 2: Hybrid CNN–BiLSTM–CTC Framework with Linguistic Refinement for Arabic Handwritten Manuscript Recognition ","10.63317\u002F5nwur655ha5k","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-35","234","238","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.35.pdf","juhaysh-etal-2026-oblevit",[3300,3303,3306],{"paper_id":3291,"author_seq":459,"given_name":3301,"surname":3302,"affiliation":135,"orcid":135},"Reem","Juhaysh",{"paper_id":3291,"author_seq":434,"given_name":3304,"surname":3305,"affiliation":135,"orcid":135},"Abuelgasim Sami","Abusonoun",{"paper_id":3291,"author_seq":408,"given_name":107,"surname":3307,"affiliation":135,"orcid":135},"Ayad","Arabic handwritten manuscript recognition is challenging due to the cursive nature of the script, dot ambiguity, and document degradation. In this work, we propose an end-to-end OCR system based on a CNN–BiLSTM–CTC architecture. The model extracts visual features, captures sequential dependencies, and performs alignment-free training. Arabic-specific decoding and post-processing techniques are applied to reduce character and spacing errors. Experimental results show competitive performance in recognizing complex handwritten Arabic text.",{"paper_id":3310,"title":3311,"year":7,"month":358,"day":135,"doi":3312,"resource_url":3313,"first_page":3314,"last_page":3315,"pdf_url":3316,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3317,"paper_type":2663,"authors":3318,"abstract":3326},"lrec2026-ws-nakbanlp-36","Viva_Palestine at StanceNakba Shared Task: Actor and Topic-Aware Stance Detection in Public Discourse ","10.63317\u002F2qfbhttoue49","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-36","239","243","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.36.pdf","elkassas-etal-2026-viva_palestine",[3319,3322,3324],{"paper_id":3310,"author_seq":459,"given_name":3320,"surname":3321,"affiliation":135,"orcid":135},"Wafaa","El-Kassas",{"paper_id":3310,"author_seq":434,"given_name":3323,"surname":2290,"affiliation":135,"orcid":135},"Enas",{"paper_id":3310,"author_seq":408,"given_name":3323,"surname":3325,"affiliation":135,"orcid":135},"El Houby","Recent research has increasingly focused on user-generated content to clarify opinions expressed in social media discourse. The Actor and Topic-Aware Stance Detection in Public Discourse challenge encourages research on stance detection in polarized social media discourse on the Palestinian–Israeli conflict. The challenge comprises two subtasks: one for actor-level alignments and the other for cross-topic generalization patterns. The StanceNakba2026 task includes two subtasks: (A) Actor-Level Stance Detection in English and (B) Cross-Topic Stance Detection in Arabic. Our team participated in both subtasks with the name \"Viva_Palestine\". In Subtask A, the proposed method is based on the Bert-Base-Uncased model and achieved a Macro F1-score of 0.9190, placing 6th out of 13 teams. In Subtask B, the proposed method is based on the MARBERT model and achieved a Macro F1-score of 0.8724 (the top rank in the leaderboard), placing first out of 10 teams. These results show that the proposed modelling method performs well for both entity-specific stance alignment and strong cross-topic generalization.",{"paper_id":3328,"title":3329,"year":7,"month":358,"day":135,"doi":3330,"resource_url":3331,"first_page":3332,"last_page":3333,"pdf_url":3334,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3335,"paper_type":2663,"authors":3336,"abstract":3347},"lrec2026-ws-nakbanlp-37","KUET at StanceNakba Shared Task: StanceMoE: Mixture-of-Experts Architecture for Stance Detection ","10.63317\u002F3pim9v9kog3d","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-37","244","251","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.37.pdf","alshafi-etal-2026-kuet",[3337,3340,3342,3344],{"paper_id":3328,"author_seq":459,"given_name":3338,"surname":3339,"affiliation":135,"orcid":135},"Abdullah","Al Shafi",{"paper_id":3328,"author_seq":434,"given_name":3341,"surname":3187,"affiliation":135,"orcid":135},"Md. Milon",{"paper_id":3328,"author_seq":408,"given_name":3343,"surname":3243,"affiliation":135,"orcid":135},"Sk. Imran",{"paper_id":3328,"author_seq":387,"given_name":3345,"surname":3346,"affiliation":135,"orcid":135},"K. M. Azharul","Hasan","Actor-level stance detection aims to determine an author’s expressed position toward specific geopolitical actors mentioned or implicated in a text. Although transformer-based models have achieved relatively good performance in stance classification, they typically rely on unified representations that may not sufficiently capture heterogeneous linguistic signals, such as contrastive discourse structures, framing cues, and salient lexical indicators. This motivates the need for adaptive architectures that explicitly model diverse stance-expressive patterns. In this paper, we propose StanceMoE, a context-enhanced Mixture-of-Experts (MoE) architecture built upon a fine-tuned BERT encoder for actor-level stance detection. Our model integrates six expert modules designed to capture complementary linguistic signals, including global semantic orientation, salient lexical cues, clause-level focus, phrase-level patterns, framing indicators, and contrast-driven discourse shifts. A context-aware gating mechanism dynamically weights expert contributions, enabling adaptive routing based on input characteristics. Experiments are conducted on the StanceNakba 2026 Subtask A dataset, comprising 1,401 annotated English texts where the target actor is implicit in the text. StanceMoE achieves a macro-F1 score of 94.26%, outperforming traditional baselines, and alternative BERT-based variants.",{"paper_id":3349,"title":3350,"year":7,"month":358,"day":135,"doi":3351,"resource_url":3352,"first_page":3353,"last_page":3354,"pdf_url":3355,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3356,"paper_type":2663,"authors":3357,"abstract":3371},"lrec2026-ws-nakbanlp-38","The Blackwell Collective at StanceNakba Shared Task: PAST-TIDE: Prototype-Anchored Statement Tuning with Topic-Invariant Normalization for Stance Detection ","10.63317\u002F4an3eqwpdtpf","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-38","252","257","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.38.pdf","shujon-etal-2026-blackwell",[3358,3361,3364,3365,3368],{"paper_id":3349,"author_seq":459,"given_name":3359,"surname":3360,"affiliation":135,"orcid":135},"Md. Shakhoyat Rahman","Shujon",{"paper_id":3349,"author_seq":434,"given_name":3362,"surname":3363,"affiliation":135,"orcid":135},"MD Jahid Hasan","Jim",{"paper_id":3349,"author_seq":408,"given_name":3341,"surname":3187,"affiliation":135,"orcid":135},{"paper_id":3349,"author_seq":387,"given_name":3366,"surname":3367,"affiliation":135,"orcid":135},"Md Rezwanul","Haque",{"paper_id":3349,"author_seq":358,"given_name":3369,"surname":3370,"affiliation":135,"orcid":135},"Fakhri","Karray","We introduce PAST-TIDE, our stance detection system addressing both subtasks of the StanceNakba Shared Task at NakbaNLP@LREC-COLING 2026. The main idea is statement tuning. We redefine stance as cloze-style masked language modeling (MLM), letting a verbalizer map label words to stance categories through the pre-trained MLM head rather than appending a randomly initialized classification head. We complement this with prototypical contrastive learning, which uses learnable class prototypes for batch-size independent contrastive training, and topic-conditional layer normalization for cross-topic Arabic stance detection. PAST-TIDE achieves macro-F1 scores of 0.75 for Subtask A and 0.74 for Subtask B on the official leaderboard, indicating that minimal architectural additions to a pre-trained model can remain competitive in low-resource settings.",{"paper_id":3373,"title":3374,"year":7,"month":358,"day":135,"doi":3375,"resource_url":3376,"first_page":3377,"last_page":3378,"pdf_url":3379,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3380,"paper_type":2663,"authors":3381,"abstract":3390},"lrec2026-ws-nakbanlp-39","AlSaifTeam at AR-MS NAKBA-NLP 2026: Building Expert-Quality Ground Truth for Arabic Handwritten Manuscripts ","10.63317\u002F5pcgw3fzbc6i","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-39","258","260","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.39.pdf","alsaif-etal-2026-alsaifteam",[3382,3385,3387],{"paper_id":3373,"author_seq":459,"given_name":3383,"surname":3384,"affiliation":135,"orcid":135},"Joud Fahad","AlSaif",{"paper_id":3373,"author_seq":434,"given_name":3386,"surname":3141,"affiliation":135,"orcid":135},"Alhasan Mohammad",{"paper_id":3373,"author_seq":408,"given_name":3388,"surname":3389,"affiliation":135,"orcid":135},"Jana Mohammad","Alseed","This paper describes our participation in Subtask 1 of the NAKBA NLP 2026 Arabic Manuscript Understanding Shared Task, which focuses on the manual creation of expert-quality, line-level transcriptions for Arabic handwritten manuscripts. To ensure reliable ground truth, we adopt a protocol-driven methodology based on fixed transcription rules, collaborative verification, and confidence-based quality control. The proposed approach aims to improve consistency, reduce annotation bias, and support the creation of trustworthy benchmark resources for future Arabic OCR and HTR research. Keywords:Arabic handwritten manuscripts, ground truth construction, manual transcription, handwritten text recognition, optical character recognition, benchmark enrichment",{"paper_id":3392,"title":3393,"year":7,"month":358,"day":135,"doi":3394,"resource_url":3395,"first_page":3396,"last_page":3397,"pdf_url":3398,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3399,"paper_type":2663,"authors":3400,"abstract":3404},"lrec2026-ws-nakbanlp-40","PalNLP at AR-MS Shared Task: Guidelines Paper for Arabic Manuscript Transcription ","10.63317\u002F3e9dvjxc2gbx","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-40","261","264","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.40.pdf","ayesh-2026-palnlp",[3401],{"paper_id":3392,"author_seq":459,"given_name":3402,"surname":3403,"affiliation":135,"orcid":135},"Mutaz","Ayesh","This paper describes the guidelines that the PalNLP team recursively developed and followed during the transcription of the assigned batch, as part of the AR-MS Shared Task. The team, which consists of a single experienced transcriber, has manually transcribed 500 images of lines from the Omar Al-Saleh Memoir Collection.",{"paper_id":3406,"title":3407,"year":7,"month":358,"day":135,"doi":3408,"resource_url":3409,"first_page":3410,"last_page":3411,"pdf_url":3412,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3413,"paper_type":2663,"authors":3414,"abstract":3447},"lrec2026-ws-nakbanlp-41","Digilians at NakbaVirality Shared Task: Bidirectional Cross-Attention for Multimodal Virality Prediction ","10.63317\u002F3tt97yspawfn","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-41","265","268","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.41.pdf","hassan-etal-2026-digilians",[3415,3417,3419,3422,3425,3427,3430,3433,3435,3437,3440,3442,3445],{"paper_id":3406,"author_seq":459,"given_name":3416,"surname":3042,"affiliation":135,"orcid":135},"Ahmed Eid",{"paper_id":3406,"author_seq":434,"given_name":3418,"surname":2968,"affiliation":135,"orcid":135},"Noureldeen H.",{"paper_id":3406,"author_seq":408,"given_name":3420,"surname":3421,"affiliation":135,"orcid":135},"Abdelrhman M.","Fawzy",{"paper_id":3406,"author_seq":387,"given_name":3423,"surname":3424,"affiliation":135,"orcid":135},"Mohamed A.","Abdelghany",{"paper_id":3406,"author_seq":358,"given_name":3426,"surname":3042,"affiliation":135,"orcid":135},"Ahmed A.",{"paper_id":3406,"author_seq":333,"given_name":3428,"surname":3429,"affiliation":135,"orcid":135},"Ahmed S.","Qassim",{"paper_id":3406,"author_seq":309,"given_name":3431,"surname":3432,"affiliation":135,"orcid":135},"Fady A.","Abd El Sayed",{"paper_id":3406,"author_seq":280,"given_name":3434,"surname":2968,"affiliation":135,"orcid":135},"Mohamed H.",{"paper_id":3406,"author_seq":252,"given_name":3436,"surname":2968,"affiliation":135,"orcid":135},"Rahma M.",{"paper_id":3406,"author_seq":224,"given_name":3438,"surname":3439,"affiliation":135,"orcid":135},"Arwa M.","Abou-Attia",{"paper_id":3406,"author_seq":193,"given_name":3441,"surname":2968,"affiliation":135,"orcid":135},"Mayar M.",{"paper_id":3406,"author_seq":161,"given_name":3443,"surname":3444,"affiliation":135,"orcid":135},"Shahd A.","Sawla",{"paper_id":3406,"author_seq":127,"given_name":3446,"surname":2669,"affiliation":135,"orcid":135},"Shahd O.","The NakbaVirality shared task focuses on multimodal virality prediction using a dataset of 2,600 multilingual posts collected from X and Reddit. In this work, we propose a multimodal architecture that combines XLM-RoBERTa for text encoding and a Vision Transformer (ViT) for image representation. The extracted features are aligned through bidirectional cross-attention to capture interactions between textual and visual modalities. To address the class imbalance present in the dataset, we apply focal loss, class weighting, and targeted data augmentation for the minority class. Additionally, layer-wise learning rate scheduling is used to stabilize fine-tuning of the pretrained encoders. Experimental results show that the proposed system achieves an accuracy of 0.6009 on the hidden test set, ranking 4th among 29 participating teams (107 total submissions). These results highlight the effectiveness of cross-modal attention mechanisms for modeling multimodal signals in high-stakes discourse.",{"paper_id":3449,"title":3450,"year":7,"month":358,"day":135,"doi":3451,"resource_url":3452,"first_page":3453,"last_page":3454,"pdf_url":3455,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3456,"paper_type":2663,"authors":3457,"abstract":3468},"lrec2026-ws-nakbanlp-42","Not Gemma at AR-MS NakbaNLP 2026: Mubsir OCR: End-to-End Recognition of Arabic Handwritten Text ","10.63317\u002F44f6f4a6cxma","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-42","269","274","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.42.pdf","ali-etal-2026-not",[3458,3460,3462,3465],{"paper_id":3449,"author_seq":459,"given_name":3459,"surname":490,"affiliation":135,"orcid":135},"Ali Adel",{"paper_id":3449,"author_seq":434,"given_name":3461,"surname":490,"affiliation":135,"orcid":135},"Mona Khaled",{"paper_id":3449,"author_seq":408,"given_name":3463,"surname":3464,"affiliation":135,"orcid":135},"Mohamed Emad","Sayed",{"paper_id":3449,"author_seq":387,"given_name":3466,"surname":3467,"affiliation":135,"orcid":135},"Ibrahim Naser","Mostafa","Historical Arabic handwritten OCR is difficult because of cursive script, fine diacritics, mixed numerals, and degraded media; classical segmentation pipelines compound errors, whereas end-to-end vision-language models can adapt when fine-tuned on in-domain data. We present Mubsir OCR, a systematic evaluation on the NAKBA dataset: an annotated set (15,962 training line crops and 2,095 val lines with ground truth, used for all nine experiments) and a separate blind AR-MS (Subtask 2) set (2,671 images; scores only via official submission). We compare external vs. in-house VLMs (Qwen2.5-VL 3B, Qwen3-VL-4B-Instruct, Gemma3), inference backends (vLLM\u002Fbf16 vs. HuggingFace\u002Fbf16), training length (16 vs. 32 epochs), and test-time preprocessing (CLAHE+unsharp). Best on the annotated val set: 8.59% CER \u002F 25.87% WER (HuggingFace bf16); the same configuration attains 11.00% CER \u002F 31.26% WER on the blind set. Domain-specific fine-tuning beats general-purpose checkpoints; preprocessing helps only marginally and is not recommended without train-time augmentation.",{"paper_id":3470,"title":3471,"year":7,"month":358,"day":135,"doi":3472,"resource_url":3473,"first_page":3474,"last_page":3475,"pdf_url":3476,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3477,"paper_type":2663,"authors":3478,"abstract":3482},"lrec2026-ws-nakbanlp-43","Latent Narratives at AR-MS NakbaNLP 2026: Reducing Character Errors in Arabic Manuscript Transcription: A CER Oriented System ","10.63317\u002F3gkghprpz2sb","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-43","275","279","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.43.pdf","aldesouky-2026-latent",[3479],{"paper_id":3470,"author_seq":459,"given_name":3480,"surname":3481,"affiliation":135,"orcid":135},"Sara Abdulmonem","Al desouky","Historic Arabic handwritten texts present significant challenges due to varied handwriting styles, cursive structure, diverse diacritics, and inconsistent character and word sizes. In this work, we introduce Historic-Arabic-OCR, a vision-language OCR system built upon Qari-OCR, which itself is based on Qwen2-VL-2B-Instruct, and further fine- tuned using Low-Rank Adaptation (LoRA) for Arabic manuscript transcription. The proposed approach incorporates contrast enhancement using CLAHE and deterministic decoding strategies to reduce character-level errors. Our model achieves competitive performance, with a Word Error Rate (WER) of 0.28 and a Character Error Rate (CER) of 0.10 on historical Arabic texts, including low-resolution images. The final submitted system uses CLAHE prepro- cessing with deterministic greedy decoding to minimize character-level errors. Keywords: Arabic OCR, Vision-Language Models, Qwen2-VL, LoRA, CER Optimization",{"paper_id":3484,"title":3485,"year":7,"month":358,"day":135,"doi":3486,"resource_url":3487,"first_page":3488,"last_page":3489,"pdf_url":3490,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3491,"paper_type":2663,"authors":3492,"abstract":3499},"lrec2026-ws-nakbanlp-44","shroukgbr at StanceNakba Shared Task: Transformer-Based Ensemble Learning for Actor-Level Stance Detection in Palestinian–Israeli Social Media Discourse ","10.63317\u002F4ej9gdhgd3vg","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-44","280","284","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.44.pdf","gbr-etal-2026-shroukgbr",[3493,3496],{"paper_id":3484,"author_seq":459,"given_name":3494,"surname":3495,"affiliation":135,"orcid":135},"Shrouk Anwar","Gbr",{"paper_id":3484,"author_seq":434,"given_name":3497,"surname":3498,"affiliation":135,"orcid":135},"Mohamed Ibrahim","Ragab","Stance detection has become an essential task for understanding political discourse on social media, particularly in highly polarized contexts where sentiment alone is insufficient to capture author intent. This study addresses stance classification in discussions related to the Palestinian–Israeli conflict by developing transformer-based and ensemble learning approaches for three-class classification: Pr-Palestine, Pro-Israel, and Neutral. Using the StanceNakba 2026 Shared Task dataset, we fine-tune multiple pretrained transformer models, including MARBERT, ARBERT, BERT, RoBERTa, and DeBERTa, and evaluate their performance using stratified cross-validation with macro F1-score as the primary metric. In addition to individual model evaluation, a weighted ensemble combining BERT, RoBERTa, and DeBERTa is proposed to leverage complementary contextual representations. Experimental results show that the ensemble model achieves the best performance with an accuracy and macro F1-score of 0.8905, outperforming specialized Arabic models while maintaining strong class-wise balance. The proposed approach achieved first place on the Codabench leaderboard in both the development and final evaluation phases of the shared task, demonstrating its robustness and effectiveness in real-world stance detection settings.",{"paper_id":3501,"title":3502,"year":7,"month":358,"day":135,"doi":3503,"resource_url":3504,"first_page":3505,"last_page":3506,"pdf_url":3507,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3508,"paper_type":2663,"authors":3509,"abstract":3514},"lrec2026-ws-nakbanlp-45","NU_Hallucinators at NakbaArchiveClassifier Shared Task: A CLIP-Based Approach for Destruction Detection in Historical Image Archives ","10.63317\u002F4c5nkazsww8o","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-45","285","289","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.45.pdf","hegazy-etal-2026-nu_hallucinators",[3510,3513],{"paper_id":3501,"author_seq":459,"given_name":3511,"surname":3512,"affiliation":135,"orcid":135},"Salma Khaled","Hegazy",{"paper_id":3501,"author_seq":434,"given_name":3497,"surname":3498,"affiliation":135,"orcid":135},"This paper presents a CLIP-based transfer learning approach for classifying historical archive images in the Nakba Image Classification Shared Task at the Nakba-NLP 2026 Workshop (LREC 2026). The task involves distinguishing images depicting destroyed or damaged infrastructure from those showing intact scenes using a dataset of 2,001 images collected from Instagram posts published by Palestinian content creators and journalists in Gaza between October 2023 and December 2025. Our method employs the CLIP ViT-B\u002F32 visual encoder with selective fine-tuning of the final transformer block and a lightweight classification head. To address class imbalance, we apply focal loss along with standard data augmentation and threshold optimization. Experimental results show that the proposed model outperforms several CNN baselines and achieves an F1-score of 0.877 on the blind test set, securing 4th place in the shared task.",{"paper_id":3516,"title":3517,"year":7,"month":358,"day":135,"doi":3518,"resource_url":3519,"first_page":3520,"last_page":3521,"pdf_url":3522,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3523,"paper_type":2663,"authors":3524,"abstract":3528},"lrec2026-ws-nakbanlp-46","ZAHIRA BOULANOUAR at NakbaArchiveClassifier Shared Task: Detecting Infrastructure Destruction in Gaza with a ConvNeXt Ensemble ","10.63317\u002F2x7rcigppzib","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-46","290","293","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.46.pdf","boulanouar-2026-zahira",[3525],{"paper_id":3516,"author_seq":459,"given_name":3526,"surname":3527,"affiliation":135,"orcid":135},"ZAHIRA","BOULANOUAR","We present our third-place submission to the Nakba Image Classification Shared Task at LREC-COLING 2026, which requires binary classification of Instagram images from Gaza into destruction (damaged or destroyed infrastructure) versus not_destruction. Our system fine-tunes a ConvNeXt-Tiny backbone within a five-fold stratified cross-validation framework, combining Focal Loss, weighted random sampling, exponential moving average (EMA) weight stabilization, test-time augmentation (TTA), and out-of-fold (OOF) decision threshold calibration. Our system achieves an official test macro F1 of 0.8893 and 90.05% accuracy, placing third among all participants and within 0.02 F1 of the winning system (0.91), demonstrating that a 28M-parameter convolutional architecture with principled training strategies is highly competitive with much larger models.",{"paper_id":3530,"title":3531,"year":7,"month":358,"day":135,"doi":3532,"resource_url":3533,"first_page":3534,"last_page":3535,"pdf_url":3536,"poster_url":135,"slide_url":135,"video_url":135,"supplementary_url":135,"bibkey":3537,"paper_type":2663,"authors":3538,"abstract":3550},"lrec2026-ws-nakbanlp-47","EGCSS at StanceNakba Shared Task: Cross-Topic Arabic Stance Detection for Two Middle East Issues ","10.63317\u002F3sxocvjww5ss","https:\u002F\u002Flrec.elra.info\u002Flrec2026-ws-nakbanlp-47","294","298","http:\u002F\u002Fwww.lrec-conf.org\u002Fproceedings\u002Flrec2026\u002Fworkshops\u002Fnakbanlp\u002Fpdf\u002F2026.nakbanlp-1.47.pdf","qindeel-etal-2026-egcss",[3539,3542,3545,3546,3549],{"paper_id":3530,"author_seq":459,"given_name":3540,"surname":3541,"affiliation":135,"orcid":135},"Asmaa","Qindeel",{"paper_id":3530,"author_seq":434,"given_name":3543,"surname":3544,"affiliation":135,"orcid":135},"Toka","Khaled",{"paper_id":3530,"author_seq":408,"given_name":2666,"surname":2667,"affiliation":135,"orcid":135},{"paper_id":3530,"author_seq":387,"given_name":3547,"surname":3548,"affiliation":135,"orcid":135},"Eman","Elrefai",{"paper_id":3530,"author_seq":358,"given_name":2669,"surname":2670,"affiliation":135,"orcid":135},"Stance detection continues to be an important task sitting at the intersection of Natural Language Processing (NLP) and Computational Social Science (CSS). In this work, we evaluate how different variations of BERT models perform on the cross-topic form of the task. In particular, we inspect their performance on the second subtask of the shared task StanceNakba 2026, where two topics are included, namely Arab Normalization with Israel and The Presence of Refugees in Arab Countries. We find that the best-performing model was bert-base-arabertv02-twitter, and we further improve its performance by providing context about the topic during the training phase, achieving an F1-score of 0.86 and ranking second among the participating teams."]