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LREC-COLING 2024main

Nested Noun Phrase Identification Using BERT

Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

DOI:10.63317/35qjsb4ywbay

Abstract

For several NLP tasks, an important substep is the identification of noun phrases in running text. This has typically been done by “chunking” – a way of finding minimal noun phrases by token classification. However, chunking-like methods do not represent the fact that noun phrases can be nested. This paper presents a novel method of finding all noun phrases in a sentence, nested to an arbitrary depth, using the BERT model for token classification. We show that our proposed method achieves very good results for both Swedish and English.

Details

Paper ID
lrec2024-main-1062
Pages
pp. 12138-12143
BibKey
misra-boye-2024-nested
Editor
N/A
Publisher
European Language Resources Association (ELRA) and ICCL
ISSN
2522-2686
ISBN
979-10-95546-34-4
Conference
Joint International Conference on Computational Linguistics, Language Resources and Evaluation
Location
Turin, Italy
Date
20 May 2024 25 May 2024

Authors

  • SM

    Shweta Misra

  • JB

    Johan Boye

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