Hierarchical Contextualized Representation for Named Entity Recognition
Table 7: Detailed results on the CoNLL-2003 dataset for IV, OOTV, OOEV, OOBV.
['[EMPTY]', '[BOLD] Baseline  [ITALIC] P', '[BOLD] Baseline  [ITALIC] R', '[BOLD] Baseline  [ITALIC] F1', '[BOLD] Ours  [ITALIC] P', '[BOLD] Ours  [ITALIC] R', '[BOLD] Ours  [ITALIC] F1']
out-of-training-vocabulary words (OOTV), out-of-embedding-vocabulary words (OOEV), and out-of-both-vocabulary words (OOBV) on CoNLL-2003 datset. According to our statistic, 63.40% / 52.43% / 84.68% of the NEs in the test set of CoNLL-2003, CoNLL-2002, and OntoNotes datasets are located in the IV part, respectively. Therefore, it is of great importance to focus on this part. We adopt memory network to memorize and retrieve the global representations and use the memorized training instances directly to participate in inference, which greatly improves both the precision and recall of the NEs in IV part, in which our model outperforms baseline by 0.39 in terms of F1 score. For OOV NEs, sentence-level representation can help these concerned tokens aware of the entire sentence, thus enhance the performance. The improvement is 0.44 / 0.43 F1 score for OOTV NEs and OOBV NEs, respectively.