Algorithm Research & Explore
|
App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 2
359-364

KENAOTE: multi-task learning model for knowledge augmented aspect and opinion pair extraction

Li Yang1,2
Tang Jiqiang3
Zhu Junwu1
Liang Mingxuan1,2
Gao Xiang1,2
1. College of Information Engineering, Yangzhou University, Yangzhou Jiangsu 225127, China
2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
3. National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing 100029, China
DOI: 10.19734/j.issn.1001-3695.2022.07.0326

Abstract

Aspect and opinion pair extraction aims to extract aspect and opinion items and match relations from a given sentence. However, related studies typically extract aspects and opinions independently without identifying the relationships. To identify the relationships of aspect and opinion item, this paper proposed a knowledge-augmented multi-task learning model for aspect and opinion pair extraction. First, it used the pre-trained language model to generate word vectors with semantic information for the text. In order to achieve the effect of knowledge enhancement, it used the masked attention mechanism to integrate the semantic information of the knowledge graph into the word vectors, and used the sequence labeling method based on the distance-based attention and conditional random fields to extract aspects and opinions. Finally, it matched the extracted aspects and opinions to predict the corresponding relationship. In order to strengthen the connection between the aspect and opinion extraction module and the matching module, the model adopted a shared coding layer to achieve joint training. In addition, in the training process, the matching module used the real labels as input, and used the result of the extraction module as input in the testing process. Finally, to demonstrate the effectiveness of the model, this paper used three general domain datasets for comparative experiments. The model achieves F1 values of 66.99%, 75.17% and 67.30% in aspect and opinion matching tasks respectively, and outperforms other comparative models.

Foundation Support

国家“242信息安全”计划资助项目(2021A008)
北京市科技新星计划交叉学科合作课题(Z191100001119014)
国家重点研发计划重点专项资助项目(2017YFC1700300,2017YFB1002300)
国家自然科学基金资助项目(61702234)
江苏省(扬州大学)研究生科研与实践创新计划资助项目(SJCX21_1551)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.07.0326
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 2
Section: Algorithm Research & Explore
Pages: 359-364
Serial Number: 1001-3695(2023)02-007-0359-06

Publish History

[2022-09-23] Accepted Paper
[2023-02-05] Printed Article

Cite This Article

李阳, 唐积强, 朱俊武, 等. KENAOTE：一种知识增强的方面和意见对提取多任务学习模型 [J]. 计算机应用研究, 2023, 40 (2): 359-364. (Li Yang, Tang Jiqiang, Zhu Junwu, et al. KENAOTE: multi-task learning model for knowledge augmented aspect and opinion pair extraction [J]. Application Research of Computers, 2023, 40 (2): 359-364. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)