Lexicon Infused Phrase Embeddings for Named Entity Resolution
Table 2: Final NER F1 scores for the CoNLL 2003 shared task. On the top are the systems presented in this paper, and on the bottom we have baseline systems. The best results within each area are highlighted in bold. Lin and Wu 2009 use massive private industrial query-log data in training.
['[BOLD] System', '[BOLD] Dev', '[BOLD] Test']
Note that using traditional unsupervised Skip-gram embeddings is worse than Brown clusters. In contrast, our lexicon-infused phrase embeddings Lex-0.01 achieves 90.90—a state-of-the-art F1 score for the test set. This result matches the highest F1 previously reported, in Lin and Wu \shortcitelin2009phrase, but is the first system to do so without using massive private data. Our result is signficantly better than the previous best using public data.