Lexicon Infused Phrase Embeddings for Named Entity Resolution
Table 1: Accuracy for Semantic-Syntactic task, when restricted to Top 30K words. Lex-0.01 refers to a model trained with lexicons, where 0.01% of negative examples were used for training.
['[BOLD] Model', '[BOLD] Accuracy']
We find that lexicon-infused embeddings perform better than Skip-gram. Further, lex-0.01 performs the best, and we use this model for further NER experiments. There was no perceptible difference in computation cost from learning lexicon-infused embeddings versus learning standard Skip-gram embeddings.