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.

本文通过重新检验增强命名实体识别的常见做法和详细分析模型性能和词表特征之间的关系，指导我们构建更合适的词表。发现：（1）词表有助于解决传统 NER 模型难以学习的情况。（2）模型性能极大地受益于高质量的预训练词汇嵌入。（3）一个好的词表应该涵盖更多在训练集和测试集中都能匹配到的实体。