<h1> TaPvex: A Tagged and Phrased word2vec Model </h1> Benjamin J. Radford <br> September 29, 2017 <h2> Summary </h2> <i>TaPvex</i> is a trained word2vec model of part-of-speech-tagged and named-entity-tagged words and phrases. The model was trained on a large corpus of English language news text from the early 2010s. Words have been tagged using Stanford CoreNLP to include <a href="https://nlp.stanford.edu/software/CRF-NER.shtml">named entities</a> (NER) and <a href="https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html">Penn Treebank</a> parts-of-speech (POS). Tagged words have been concatenated into n-gram phrases.<br> The model contains 1.17 million unique words and phrases. Word vectors are of size 150. <h2> Use </h2> All three files (TaPvex, TaPvex.syn0.npy, TaPvex.syn1.npy) must be located in the same directory. The file can be opened with the <a href="https://radimrehurek.com/gensim/models/word2vec.html"><i>gensim</i></a> Python module using: <pre> from gensim.models import Word2Vec model = Word2Vec.load("/path/to/model/TaPvex") </pre> <h2> Examples </h2> Tokens are of the form: <pre>[WORD]:[NER]:[POS]</pre> Phrases are of the form: <pre>[WORD]:[NER]:[POS]_[WORD]:[NER]:[POS]</pre> Example tokens include: <pre> BUSH:O:NN BUSHES:O:NNS BUSH:PERSON:NNP GEORGE:PERSON:NNP_BUSH:PERSON:NNP GEORGE:PERSON:NNP_W:PERSON:NNP_BUSH:PERSON:NNP NEW:O:JJ NEW:LOCATION:NNP_YORK:LOCATION:NNP NEW:ORGANIZATION:NNP_YORK:ORGANIZATION:NNP_TIMES:ORGANIZATION:NNP </pre>