DSMER: A Deep Semantic Matching Based Framework for Named Entity Recognition

Abstract

The task of named entity recognition (NER) is normally regarded as a sequence labeling problem. However, this kind of NER framework does not utilize any prior knowledge. In this paper, we propose a novel framework called DSMER, which stands for Deep Semantic Matching based Framework for Named Entity Recognition. DSMER is a two-phase framework: 1) detect the boundary and extract candidate spans, 2) calculate the distance between candidates and entity type. Meanwhile, the representation of each entity type is encoded from its corresponding annotation rules and example set. Since the combination of various textual data, DSMER has the ability to integrate informative prior knowledge. Additionally, we introduce the Word Mover’s Distance to measure the similarity between sequences of different lengths. We conduct experiments on CoNLL 2003 and OntoNotes 5.0 dataset. Experimental result shows our approach achieve state of the art performance, and demonstrates the effectiveness of the proposed framework.

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Citation

@inproceedings{lyu2021dsmer,
  title={DSMER: A Deep Semantic Matching Based Framework for Named Entity Recognition},
  author={Lyu, Yufeng and Zhong, Jiang},
  booktitle={European Conference on Information Retrieval},
  pages={419--432},
  year={2021},
  organization={Springer}
}