Abstract

Recurrent neural networks (RNN) used for Chinese named entity recognition (NER) that sequentially track character and word information have achieved great success. However, the characteristic of chain structure and the lack of global semantics determine that RNN-based models are vulnerable to word ambiguities. In this work, we try to alleviate this problem by introducing a lexicon-based graph neural network with global semantics, in which lexicon knowledge is used to connect characters to capture the local composition, while a global relay node can capture global sentence semantics and long-range dependency. Based on the multiple graph-based interactions among characters, potential words, and the whole-sentence semantics, word ambiguities can be effectively tackled. Experiments on four NER datasets show that the proposed model achieves significant improvements against other baseline models.

Highlights

  • The task of named entity recognition (NER) involves determining entity boundaries and recognizing categories of named entities, which is a fundamental task in the field of natural language processing (NLP)

  • We introduce a lexicon-based graph neural network (LGN) that achieves Chinese NER as a node classification task

  • The main contributions of this paper can be summarized as follows: 1) we propose the use of a lexicon to construct a graph neural network and achieve Chinese NER as a graph node classification task; 2) the proposed model can capture global context information and local compositions to tackle Chinese word ambiguity problems through recursively aggregating mechanism; 3) several experimental results demonstrate the effectiveness of the proposed method in different aspects

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Summary

Introduction

The task of named entity recognition (NER) involves determining entity boundaries and recognizing categories of named entities, which is a fundamental task in the field of natural language processing (NLP). Compared with English NER, Chinese named entities are more difficult to identify due to their uncertain boundaries, complex composition, and NE definitions within the nest (Duan and Zheng, 2011).

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