sense2vec (Trask et. al, 2015) is a nice twist on word2vec that lets you learn more interesting, detailed and context-sensitive word vectors. For an interactive example of the technology, see our sense2vec demo that lets you explore semantic similarities across all Reddit comments of 2015.
import spacy from sense2vec import Sense2VecComponent nlp = spacy.load('en') s2v = Sense2VecComponent('/path/to/reddit_vectors-1.1.0') nlp.add_pipe(s2v) doc = nlp("A sentence about natural language processing.") assert doc.text == 'natural language processing' freq = doc._.s2v_freq vector = doc._.s2v_vec most_similar = doc._.s2v_most_similar(3) # [(('natural language processing', 'NOUN'), 1.0), # (('machine learning', 'NOUN'), 0.8986966609954834), # (('computer vision', 'NOUN'), 0.8636297583580017)]
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