A Novel Dual Pointer Approach for Entity Mention Extraction. Issue 1 (1st January 2021)

• Record Type: Journal Article
• Authors: Jie, Liu; Yihe, Pang; Kai, Zhang; Lizhen, Liu; Zhengtao, Yu
• Other Names: Liu Datong guestEditor.; Zhang Bin guestEditor.; (Peter) Liu Jie guestEditor.; Boland David guestEditor.
• Is Part Of: Chinese journal of electronics. Volume 30:Issue 1(2021)
• Page Start: 127
• Page End: 133
• Publication Date: 2021-01-01
• Subjects: Dual pointer approach -- Entity extraction -- Deep neural networks -- Natural language processing; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2020.11.010
• Languages: English
• ISSNs: 1022-4653

Abstract

Abstract : 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 task. This 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 pointers. The evaluation results on the Chinese public dataset show that the model achieves the state‐of‐the‐art results over the baseline models.