Parsing Early Modern English for Linguistic Search
This work addresses the need for linguists to efficiently search and analyze historical English texts, though it is incremental as it combines existing NLP methods for a specific domain.
The authors tackled the problem of enabling large-scale linguistic research in historical syntax by applying modern NLP tools to automatically parse Early Modern English, achieving improved query accuracy on parsed historical corpora.
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.