SLK-NER: Exploiting Second-order Lexicon Knowledge for Chinese NER
TABLE III: Experimental results (%) of different encoding strategies on three datasets.
['[BOLD] Encoding Strategy', '[BOLD] OntoNotes4 P', '[BOLD] OntoNotes4 R', '[BOLD] OntoNotes4 F1', '[BOLD] Weibo P', '[BOLD] Weibo R', '[BOLD] Weibo F1', '[BOLD] Resume P', '[BOLD] Resume R', '[BOLD] Resume F1']
Iv-C1 Lexicon Knowledge Types We conduct comparative experiments on different kinds of lexicon knowledge. We can clearly see that the character-based model performs poorly without lexicon knowledge, demonstrating the usefulness of lexicon. Besides, adding FLK makes a small improvement on F1. While adding SLK outperforms significantly on F1 in all datasets. The fact demonstrates the efficiency of SLK, and reveals that leveraging second-order lexicon knowledge can indeed alleviate the word boundary conflicts. Interestingly, when using both FLK and SLK, the F1 declines over three datasets. We conjecture the reason is there may be some negative word conflicts simultaneously for a character which limit the performance.