Survey of Chinese Named Entity Recognition

Abstract

The Chinese named entity recognition (NER) task is a sub-task within the information extraction domain, where the task goal is to find, identify and classify relevant entities, such as names of people, places and organizations, from sentences given a piece of unstructured text. Chinese named entity recognition is a fundamental task in the field of natural language processing (NLP) and plays an important role in many downstream NLP tasks, including information retrieval, relationship extraction and question and answer systems. This paper provides a comprehensive review of existing neural network-based word-character lattice structures for Chinese NER models. Firstly, this paper introduces that Chinese NER is more difficult than English NER, and there are difficulties and challenges such as difficulty in determining the boundaries of Chinese text-related entities and complex Chinese grammatical structures. Secondly, this paper investigates the most representative lattice-structured Chinese NER models under different neural network architectures (RNN (recurrent neural network), CNN (convolutional neural network), GNN (graph neural network) and Transformer). Since word sequence information can capture more boundary information for character-based sequence learning, in order to explicitly exploit the lexical information associated with each character, some prior work has proposed integrating word information into character sequences via word-character lattice structures. These neural network-based word-character lattice structures perform significantly better than word-based or character-based approaches on the Chinese NER task. Finally, this paper introduces the dataset and evaluation criteria of Chinese NER.

Keywords

• |named entity recognition (ner)|lattice structure|neural network