SLK-NER: Exploiting Second-order Lexicon Knowledge for Chinese NER
TABLE II: Experimental results(%) on three datasets.
['[BOLD] Models', '[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']
Firstly, we compare SLK-NER with three general sequence labeling model for NER. All of them performed without any lexicon knowledge. SLK-NER gives better F1 and R than LGN, MG-GNN and CGN. Although these baselines explore lexicon knowledge via the graph structure, they performed without the consideration of contextual information. Hence, we attribute the benefits to the efficiency of global context-aware in SLK-NER. We can observe that our proposed model achieves a remarkably improvement on F1 over three datasets. The results strongly verify the integrating SLK into character-based model enables to boost the performance. By leveraging the SLK properly, our model is capable of improving NER in various domains, such as social network, news and Chinese resume.