Rethinking the Value of Gazetteer in Chinese Named Entity Recognition
This work addresses the need for a systematic evaluation of gazetteer-enhanced models in Chinese NER, providing insights for building better gazetteers, but it is incremental as it builds on existing practices without introducing a new paradigm.
The paper systematically analyzes the effectiveness of gazetteers in Chinese named entity recognition, finding that they improve performance in cases where traditional models struggle and that high-quality pre-trained lexeme embeddings and coverage of entities in both training and testing sets are key factors.
Gazetteer is widely used in Chinese named entity recognition (NER) to enhance span boundary detection and type classification. However, to further understand the generalizability and effectiveness of gazetteers, the NLP community still lacks a systematic analysis of the gazetteer-enhanced NER model. In this paper, we first re-examine the effectiveness several common practices of the gazetteer-enhanced NER models and carry out a series of detailed analysis to evaluate the relationship between the model performance and the gazetteer characteristics, which can guide us to build a more suitable gazetteer. The findings of this paper are as follows: (1) the gazetteer improves most of the situations that the traditional NER model datasets are difficult to learn. (2) the performance of model greatly benefits from the high-quality pre-trained lexeme embeddings. (3) a good gazetteer should cover more entities that can be matched in both the training set and testing set.