  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.
