Volume 30 Issue 1
Jan.  2021
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Article Contents
LIU Jie, PANG Yihe, ZHANG Kai, LIU Lizhen, YU Zhengtao. A Novel Dual Pointer Approach for Entity Mention Extraction[J]. Chinese Journal of Electronics, 2021, 30(1): 127-133. doi: 10.1049/cje.2020.11.010
Citation: LIU Jie, PANG Yihe, ZHANG Kai, LIU Lizhen, YU Zhengtao. A Novel Dual Pointer Approach for Entity Mention Extraction[J]. Chinese Journal of Electronics, 2021, 30(1): 127-133. doi: 10.1049/cje.2020.11.010

A Novel Dual Pointer Approach for Entity Mention Extraction

doi: 10.1049/cje.2020.11.010
Funds:

the Key Special Projects of National Key R & D Program of China 2018YFC0830100

National Natural Science Foundation of China 61672361

National Natural Science Foundation of China 62076167

Beijing Municipal Education Commission-Beijing Natural Fund Joint Funding Project KZ201910028039

More Information
  • Author Bio:

    PANG Yihe  was born in July, 1995. She is a Postgraduate in the Department of Information and Engineering, Capital Normal University, Beijing, China. Her research interests include Machine learning, deep learning, natural language processing, knowledge graph. (Email: 2171002020@cnu.edu.cn)

    ZHANG Kai  is a senior engineer and he works at the China Language Intelligence Research Center. He is a Ph.D. candidate in the direction of language intelligence. His research direction is natural language processing

    LIU Lizhen  is working in the Department of Information Engineering, Capital Normal University, graduated from Beijing Institute of Technology with a Ph.D. degree in Computer Application Technology. She is Deputy Secretary General of the Education Working Committee of the China Artificial Intelligence Society, and Member of the Social Media Processing Committee of the Chinese Information Society of China. Her Research direction include natural language processing, data mining

    YU Zhengtao  is a professor and Dean of the School of Information Engineering and Automation, Kunming University of Science and Technology, and Chief Professor of Intelligent Information Processing Innovation Team of Yunnan Province. He graduated from Beijing Institute of Technology in 2005 with a Ph.D. degree in Computer Application Technology from 2008. From December to December 2009, mainly engaged in natural language processing, information retrieval, machine learning and machine translation. Member of the Chinese Information Technology Committee of the Chinese Computer Society, member of the Chinese Computer Society's Pattern Recognition and Artificial Intelligence Committee, m China Member of the Intelligent Learning Machine Learning Committee

  • Corresponding author: LIU Jie  (corresponding author)  received Ph.D. degree in computer application technology from Beijing Institute of Technology, China, in 2009. From April 1999, he has been working in the Department of Information and Engineering, Capital Normal University, Beijing, P. R. China. He is currently a professor. His current research interests include computational intelligence, natural language processing and semantic web. He is a member of China CCF. (Email: liujie@cnu.edu.cn)
  • Received Date: 2019-08-15
  • Accepted Date: 2019-09-16
  • Publish Date: 2021-01-01
  • 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.
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