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arXiv · 1906.08449

Multi-Grained Named Entity Recognition

Congying Xia·Chenwei Zhang·Tao Yang·Yaliang Li·Nan Du·Xian Wu·Wei Fan·Fenglong Ma·Philip Yu
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Abstract

This paper presents a novel framework, MGNER, for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested. Different from traditional approaches regarding NER as a sequential labeling task and annotate entities consecutively, MGNER detects and recognizes entities on multiple granularities: it is able to recognize named entities without explicitly assuming non-overlapping or totally nested structures. MGNER consists of a Detector that examines all possible word segments and a Classifier that categorizes entities. In addition, contextual information and a self-attention mechanism are utilized throughout the framework to improve the NER performance. Experimental results show that MGNER outperforms current state-of-the-art baselines up to 4.4% in terms of the F1 score among nested/non-overlapping NER tasks.

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Congying Xia, Chenwei Zhang, Tao Yang, Yaliang Li, Nan Du, Xian Wu, Wei Fan, Fenglong Ma, Philip Yu. 2019-06-20. Multi-Grained Named Entity Recognition. https://doi.org/10.18653/v1%2Fp19-1138

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