Parsing Early Modern English for Linguistic Search

2020-02-24 · SCiL 2022 2 · Seth Kulick, Neville Ryant

We investigate the question of whether advances in NLP over the last few years make it possible to vastly increase the size of data usable for research in historical syntax. This brings together many of the usual tools in NLP - word embeddings, tagging, and parsing - in the service of linguistic queries over automatically annotated corpora. We train a part-of-speech (POS) tagger and parser on a corpus of historical English, using ELMo embeddings trained over a billion words of similar text. The evaluation is based on the standard metrics, as well as on the accuracy of the query searches using the parsed data.

📄 PDF Abstract BibTeX arXiv:2002.10546

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POSWord Embeddings

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