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Our submission to the W-NUT Named Entity Recognition in Twitter task closely follows the approach detailed by Cherry and Guo (2015), who use a discriminative, semi-Markov tagger, augmented with multiple word representations. We enhance this approach with updated gazetteers, and with infused phrase embeddings that have been adapted to better predict the gazetteer membership of each phrase. Our system achieves a typed F1 of 44.7, resulting in a third-place finish, despite training only on the official training set. A post-competition analysis indicates that also training on the provided development data improves our performance to 54.2 F1.

NRC: Infused Phrase Vectors for Named Entity Recognition in Twitter