Chinese Named Entity Recognition Augmented with Lexicon Memory  

作　　者：周奕 郑骁庆 黄萱菁 Yi Zhou;Xiao-Qing Zheng;Xuan-Jing Huang(School of Computer Science,Fudan University,Shanghai 200438,China;Shanghai Key Laboratory of Intelligent Information Processing,Shanghai 200438,China)

机构地区：[1]School of Computer Science,Fudan University,Shanghai 200438,China [2]Shanghai Key Laboratory of Intelligent Information Processing,Shanghai 200438,China

出　　处：《Journal of Computer Science & Technology》2023年第5期1021-1035,共15页计算机科学技术学报（英文版）

基　　金：supported by the National Key Research and Development Program of China under Grant No.2018YFC0830900;the National Natural Science Foundation of China under Grant No.62076068;Shanghai Municipal Science and Technology Project under Grant No.21511102800。

摘　　要：Inspired by the concept of content-addressable retrieval from cognitive science,we propose a novel fragment-based Chinese named entity recognition(NER)model augmented with a lexicon-based memory in which both character-level 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 Fl-score up to 3.2%over the state-of-the-art models on four different widely-used NER datasets.

关 键 词：named entity recognition(NER) lexicon-based memory content-addressable retrieval position-dependent feature neural network 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]