Research on Multi-head Self-attention Aspect Term Extraction with Multi-level Features Encoding

Zhang Penghui; Yang Peng

doi:10.2991/978-94-6463-046-6_45

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Research on Multi-head Self-attention Aspect Term Extraction with Multi-level Features Encoding

Authors

Zhang Penghui¹^{, 3}^{, *}, Yang Peng¹^{, 2}^{, 3}

¹School of Cyber Science and Engineering, Southeast University, Nanjing, 210000, China

²School of Computer Science and Engineering, Southeast University, Nanjing, 211189, China

³Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, Nanjing, 211189, China

^*Corresponding author. Email: zhangpenghui@seu.edu.cn

Corresponding Author

Zhang Penghui

Available Online 17 December 2022.

DOI: 10.2991/978-94-6463-046-6_45 How to use a DOI?
Keywords: Aspect-Based Sentiment Analysis; Aspect Term Extraction; Msa-Fmf
Abstract: With the development of social media and e-commerce, aspect-based sentiment analysis technique is more and more urgently needed. Aspect term extraction is an important part of aspect-based sentiment analysis, and the quality of the extracted aspect words directly determines the quality of aspect-based sentiment analysis. To solve the problems of current research, in this paper, we design and implement a multi-head self-attention aspect term extraction fusing with multi-level features model (MSA-FMF). In addition, we conduct experiments on three public datasets, and the proposed model outperforms the comparison models, proving the effectiveness of our model.
Copyright: © 2023 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the 2022 2nd International Conference on Computer Technology and Media Convergence Design (CTMCD 2022)
Series: Advances in Computer Science Research
Publication Date: 17 December 2022
ISBN: 978-94-6463-046-6
ISSN: 2352-538X
DOI: 10.2991/978-94-6463-046-6_45 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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ris enw bib

TY  - CONF
AU  - Zhang Penghui
AU  - Yang Peng
PY  - 2022
DA  - 2022/12/17
TI  - Research on Multi-head Self-attention Aspect Term Extraction with Multi-level Features Encoding
BT  - Proceedings of the 2022 2nd International Conference on Computer Technology and Media Convergence Design (CTMCD 2022)
PB  - Atlantis Press
SP  - 380
EP  - 389
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-046-6_45
DO  - 10.2991/978-94-6463-046-6_45
ID  - Penghui2022
ER  -

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