Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information
This work addresses the challenge of effectively using syntactic knowledge in NER for improved accuracy in natural language processing tasks, representing an incremental improvement.
The paper tackles the problem of named entity recognition by leveraging syntactic information through an attentive ensemble, resulting in outperforming previous studies on six benchmark datasets.
Named entity recognition (NER) is highly sensitive to sentential syntactic and semantic properties where entities may be extracted according to how they are used and placed in the running text. To model such properties, one could rely on existing resources to providing helpful knowledge to the NER task; some existing studies proved the effectiveness of doing so, and yet are limited in appropriately leveraging the knowledge such as distinguishing the important ones for particular context. In this paper, we improve NER by leveraging different types of syntactic information through attentive ensemble, which functionalizes by the proposed key-value memory networks, syntax attention, and the gate mechanism for encoding, weighting and aggregating such syntactic information, respectively. Experimental results on six English and Chinese benchmark datasets suggest the effectiveness of the proposed model and show that it outperforms previous studies on all experiment datasets.