Nested Named Entity Recognition via Second-best Sequence Learning and Decoding
Table 3: Main results. We group methods into three types. The first group consists of the methods that do not use any contextual word embeddings. The second group consists of the methods that use BERT but do not use any other contextual word embeddings. The third group consists of the methods that use both BERT and FLAIR. “†” indicates the methods using POS tags.
['[BOLD] Method', '[BOLD] ACE-2005 Precision (%)', '[BOLD] ACE-2005 Recall (%)', '[BOLD] ACE-2005 F1 (%)', '[BOLD] GENIA Precision (%)', '[BOLD] GENIA Recall (%)', '[BOLD] GENIA F1 (%)']
Note that some existing methods use embeddings of POS tags as an additional input feature whereas our method does not. Our method outperforms the existing methods with 76.83% and 77.19% in terms of F1-score when using only word embeddings and character-level representation. Especially, our method brings much higher recall values than the other methods. The recall scores are improved by 3.1% and 2.4% on ACE-2005 and GENIA datasets, respectively. These results demonstrate that our training and decoding algorithms are quite effective for extracting nested entities. Moreover, when we use BERT and FLAIR as contextual word embeddings, we achieve an F1-score of 83.99% with BERT and 84.34% with BERT and FLAIR on ACE-2005. On the other hand, BERT does not perform well on GENIA. We assume that this is because the domain of GENIA is quite different from that of the corpus used for training the BERT model. Regardless, it is demonstrated that our method performs better than or at least as well as existing methods.