Nested Named Entity Recognition via Second-best Sequence Learning and Decoding
Table 4: Results when ablating away the learning (L) and decoding (D) components of our proposed method.
['[EMPTY]', '[BOLD] ACE-2005 Precision (%)', '[BOLD] ACE-2005 Recall (%)', '[BOLD] ACE-2005 F1 (%)', '[BOLD] GENIA Precision (%)', '[BOLD] GENIA Recall (%)', '[BOLD] GENIA F1 (%)']
They demonstrate that introducing only our decoding algorithm surely brings high recall scores but hurts precision. This suggests that our learning method should be necessary for achieving high precision. Besides, removing the decoding algorithm results in lower recall. This is natural because it does not intend to find any nested entity after extracting outermost entities. Thus, it is demonstrated that both our learning and decoding algorithms contribute much to good performance.