Neural Architectures for Named Entity Recognition
Table 1: English NER results (CoNLL-2003 test set). * indicates models trained with the use of external labeled data
['[BOLD] Model', '[BOLD] F [BOLD] 1']
To make the comparison between our model and others fair, we report the scores of other models with and without the use of external labeled data such as gazetteers and knowledge bases. Our models do not use gazetteers or any external labeled resources. The best score reported on this task is by \newciteluojoint. They obtained a F1 of 91.2 by jointly modeling the NER and entity linking tasks [hoffart2011robust]. Their model uses a lot of hand-engineered features including spelling features, WordNet clusters, Brown clusters, POS tags, chunks tags, as well as stemming and external knowledge bases like Freebase and Wikipedia. Our LSTM-CRF model outperforms all other systems, including the ones using external labeled data like gazetteers. Our Stack-LSTM model also outperforms all previous models that do not incorporate external features, apart from the one presented by \newcitechiu2015named.