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Named entity recognition (NER) is highly sensitive to sentential syntacticand semantic properties where entities may be extracted according to how theyare used and placed in the running text. To model such properties, one couldrely on existing resources to providing helpful knowledge to the NER task; someexisting studies proved the effectiveness of doing so, and yet are limited inappropriately leveraging the knowledge such as distinguishing the importantones for particular context. In this paper, we improve NER by leveragingdifferent types of syntactic information through attentive ensemble, whichfunctionalizes by the proposed key-value memory networks, syntax attention, andthe gate mechanism for encoding, weighting and aggregating such syntacticinformation, respectively. Experimental results on six English and Chinesebenchmark datasets suggest the effectiveness of the proposed model and showthat it outperforms previous studies on all experiment datasets.

Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information