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Beta distribution guided aspect-aware graph for aspect category sentiment analysis with affective knowledge
Tools
Liang, Bin, Su, Hang, Yin, Rongdi, Gui, Lin, Yang, Min, Zhao, Qin, Yu, Xiaoqi and Xu, Ruifeng (2021) Beta distribution guided aspect-aware graph for aspect category sentiment analysis with affective knowledge. In: The 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021), Online ; Punta Cana, Dominican Republic, 7–11 Nov 2021. Published in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing pp. 208-218.
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Official URL: https://aclanthology.org/2021.emnlp-main.19
Abstract
In this paper, we investigate the Aspect Category Sentiment Analysis (ACSA) task from a novel perspective by exploring a Beta Distribution guided aspect-aware graph construction based on external knowledge. That is, we are no longer entangled about how to laboriously search the sentiment clues for coarsegrained aspects from the context, but how to preferably find the words highly sentimentrelated to the aspects in the context and determine their importance based on the public knowledge base, so as to naturally learn the aspect-related contextual sentiment dependencies with these words in ACSA. To be specific, we first regard each aspect as a pivot to derive aspect-aware words that are highly related to the aspect from external affective commonsense knowledge. Then, we employ Beta Distribution to educe the aspect-aware weight, which reflects the importance to the aspect, for each aspect-aware word. Afterward, the aspect-aware words are served as the substitutes of the coarse-grained aspect to construct graphs for leveraging the aspectrelated contextual sentiment dependencies in ACSA. Experiments on 6 benchmark datasets show that our approach significantly outperforms the state-of-the-art baseline methods.
Item Type: | Conference Item (Paper) | |||||||||||||||||||||||||||||||||
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Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||||||||||||||||||||||||||
Library of Congress Subject Headings (LCSH): | Sentiment analysis , Computational linguistics, Data mining, Machine learning, Text processing (Computer science) | |||||||||||||||||||||||||||||||||
Journal or Publication Title: | Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing | |||||||||||||||||||||||||||||||||
Publisher: | Association for Computational Linguistics | |||||||||||||||||||||||||||||||||
Official Date: | November 2021 | |||||||||||||||||||||||||||||||||
Dates: |
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Page Range: | pp. 208-218 | |||||||||||||||||||||||||||||||||
Status: | Peer Reviewed | |||||||||||||||||||||||||||||||||
Publication Status: | Published | |||||||||||||||||||||||||||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||||||||||||||||||||
Date of first compliant deposit: | 4 November 2021 | |||||||||||||||||||||||||||||||||
Date of first compliant Open Access: | 9 November 2021 | |||||||||||||||||||||||||||||||||
RIOXX Funder/Project Grant: |
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Conference Paper Type: | Paper | |||||||||||||||||||||||||||||||||
Title of Event: | The 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021) | |||||||||||||||||||||||||||||||||
Type of Event: | Conference | |||||||||||||||||||||||||||||||||
Location of Event: | Online ; Punta Cana, Dominican Republic | |||||||||||||||||||||||||||||||||
Date(s) of Event: | 7–11 Nov 2021 | |||||||||||||||||||||||||||||||||
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