  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.
