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Lexical and Semantic Features for Cross-lingual Text Reuse Classification: an Experiment in English and Latin Paraphrases

Analyzing historical languages, such as Ancient Greek and Latin, is challenging. Such languages are often under-resourced and lack primary material for certain time periods. This prevents applying advanced natural-language processing (NLP) techniques and requires resorting to basic NLP not relying on machine learning. An important analysis is the discovery and classification of paraphrastic text reuse in historical languages. This reuse is often paraphrastic and challenges basic NLP techniques. Our goal is to improve the applicability of advanced NLP techniques on historical text reuse. We present an experiment of cross-applying classifiers—that we trained for paraphrase recognition on modern English text corpora—on historical texts. We analyze the impact of four different lexical and semantic features, on the resulting reuse-detection accuracy. We find out that—against initial conjecture—word embedding can help to drastically improve accuracy if lexical features (such as the overlap of similar words) fail.