A Novel Dual Pointer Approach for Entity Mention Extraction
A Novel Dual Pointer Approach for Entity Mention Extraction作者机构：Department of Information Engineering Capital Normal University Research Center for Language Intelligence Capital Normal University Department of Information Engineering and Automation Kunming University of Science and Technology
出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))
年 卷 期：2021年第30卷第1期
页 面：127-133页
核心收录：
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 081203[工学-计算机应用技术] 08[工学] 081104[工学-模式识别与智能系统] 0835[工学-软件工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by the Key Special Projects of National Key R&D Program of China (No.2018YFC0830100) National Natural Science Foundation of China (No.61672361, No.62076167) Beijing Municipal Education Commission-Beijing Natural Fund Joint Funding Project (No.KZ201910028039)
主 题：Dual pointer approach Entity extraction Deep neural networks Natural language processing
摘 要：The named entity extraction task aims to extract entity mentions from the unstructured text,including names of people, places, institutions and so on. It plays an important role in many Natural language processing(NLP) tasks, such as knowledge bases construction, automatic question answering system and information extraction. Most of the existing entity extraction studies are based on the long text data, which are easier to annotate due to the sufficient contextual information. Extracting entities from short texts such as search queries, conversations is still a challenging *** paper proposes a dual pointer approach for entity mention extraction, it extracts one entities by two position pointers of the input sentence. The end-to-end deep neural networks model based on the proposed approach can extract the entities by serially generating the dual *** evaluation results on the Chinese public dataset show that the model achieves the state-of-the-art results over the baseline models.
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