Chinese Named Entity Recognition Augmented with Lexicon Memory

IF 1.2 3区 计算机科学 Q4 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Journal of Computer Science and Technology Pub Date : 2023-09-30 DOI:10.1007/s11390-021-1153-y
Yi Zhou, Xiao-Qing Zheng, Xuan-Jing Huang
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引用次数: 4

Abstract

Inspired by the concept of content-addressable retrieval from cognitive science, we propose a novel fragmentbased Chinese named entity recognition (NER) model augmented with a lexicon-based memory in which both characterlevel and word-level features are combined to generate better feature representations for possible entity names. Observing that the boundary information of entity names is particularly useful to locate and classify them into pre-defined categories, position-dependent features, such as prefix and suffix, are introduced and taken into account for NER tasks in the form of distributed representations. The lexicon-based memory is built to help generate such position-dependent features and deal with the problem of out-of-vocabulary words. Experimental results show that the proposed model, called LEMON, achieved state-of-the-art performance with an increase in the F1-score up to 3.2% over the state-of-the-art models on four different widely-used NER datasets.

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词汇记忆增强的中文命名实体识别
受认知科学中内容可寻址检索概念的启发,我们提出了一种新的基于片段的中文命名实体识别(NER)模型,该模型增强了基于词汇的记忆,将字符级和词级特征结合起来,为可能的实体名称生成更好的特征表示。观察到实体名称的边界信息对于将它们定位和分类到预定义的类别特别有用,因此以分布式表示的形式引入并考虑了NER任务的位置相关特征,如前缀和后缀。基于词汇的记忆是为了帮助生成这种位置相关的特征,并解决词汇表外的问题。实验结果表明,所提出的模型(称为LEMON)在四个不同的广泛使用的NER数据集上取得了最先进的性能,f1分数比最先进的模型提高了3.2%。
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来源期刊
Journal of Computer Science and Technology
Journal of Computer Science and Technology 工程技术-计算机:软件工程
CiteScore
4.00
自引率
0.00%
发文量
2255
审稿时长
9.8 months
期刊介绍: Journal of Computer Science and Technology (JCST), the first English language journal in the computer field published in China, is an international forum for scientists and engineers involved in all aspects of computer science and technology to publish high quality and refereed papers. Papers reporting original research and innovative applications from all parts of the world are welcome. Papers for publication in the journal are selected through rigorous peer review, to ensure originality, timeliness, relevance, and readability. While the journal emphasizes the publication of previously unpublished materials, selected conference papers with exceptional merit that require wider exposure are, at the discretion of the editors, also published, provided they meet the journal''s peer review standards. The journal also seeks clearly written survey and review articles from experts in the field, to promote insightful understanding of the state-of-the-art and technology trends. Topics covered by Journal of Computer Science and Technology include but are not limited to: -Computer Architecture and Systems -Artificial Intelligence and Pattern Recognition -Computer Networks and Distributed Computing -Computer Graphics and Multimedia -Software Systems -Data Management and Data Mining -Theory and Algorithms -Emerging Areas
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