再论地名词典在中文命名实体识别中的价值
Rethinking the Value of Gazetteer in Chinese Named Entity Recognition
摘要：
地名词典被广泛应用于中文命名实体识别（NER）中，以增强跨边界检测和类型分类。然而，为了进一步了解地名录的普遍性和有效性，自然语言处理界仍然缺乏对地名录增强的净入学率模型的系统分析。本文首先重新审视了几种常见的地名词典增强NER模型的有效性，并进行了一系列详细的分析，以评估模型性能与地名词典特征之间的关系，从而指导我们构建更适合的地名词典。本文的研究结果如下：（1）地名录改善了传统NER模型数据集难以学习的大部分情况。(2)模型的性能大大受益于高质量的预训练词位嵌入。(3)一个好的地名录应涵盖更多的实体，这些实体在训练集和测试集中都可以匹配。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
