Multi-feature Interaction for Aspect Sentiment Triplet Extraction
Aspect sentiment triple extraction is one of the subtasks of aspect-level sentiment analysis, which aims to extract aspect terms, corresponding opinion terms and sentiment polarity in sentence. Previous studies focus on designing a new paradigm to complete the triplet extraction task in an end-to-en...
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| Format: | Article |
| Language: | zho |
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2024-04-01
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| Series: | Jisuanji kexue yu tansuo |
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| Online Access: | http://fcst.ceaj.org/fileup/1673-9418/PDF/2302077 .pdf |
| _version_ | 1827299673395691520 |
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| author | CHEN Linying, LIU Jianhua, ZHENG Zhixiong, LIN Jie, XU Ge, SUN Shuihua |
| author_facet | CHEN Linying, LIU Jianhua, ZHENG Zhixiong, LIN Jie, XU Ge, SUN Shuihua |
| author_sort | CHEN Linying, LIU Jianhua, ZHENG Zhixiong, LIN Jie, XU Ge, SUN Shuihua |
| collection | DOAJ |
| description | Aspect sentiment triple extraction is one of the subtasks of aspect-level sentiment analysis, which aims to extract aspect terms, corresponding opinion terms and sentiment polarity in sentence. Previous studies focus on designing a new paradigm to complete the triplet extraction task in an end-to-end manner. They ignore the role of external knowledge in the model, thus semantic information, part-of-speech information and local context information are not fully explored and utilized. Aiming at the above problems, multi-feature interaction for aspect sentiment triplet extraction (MFI-ASTE) is proposed. Firstly, the bidirectional encoder representation from transformers (BERT) model is used to learn the context semantic feature information, meanwhile, the self-attention mechanism is used to strengthen the semantic feature. Secondly, the semantic feature interacts with the extracted part-of-speech feature and both learn from each other to strengthen the combination ability of the part-of-speech and semantic information. Thirdly, many convolutional neural networks are used to extract multiple local context features of each word, and then multi-point gate mechanism is used to filter these features. Fourthly, three features of external knowledge are fused by two linear layers. Finally, biaffine attention is used for predicting grid tagging and specific decoding schemes are used for decoding triplets. Experimental results show that the proposed model improves the F1 score by 6.83%, 5.60%, 0.54% and 1.22% respectively on four datasets compared with existing mainstream models. |
| first_indexed | 2024-04-24T15:37:43Z |
| format | Article |
| id | doaj.art-c4730e1084f441afa52944c064513d3b |
| institution | Directory Open Access Journal |
| issn | 1673-9418 |
| language | zho |
| last_indexed | 2024-04-24T15:37:43Z |
| publishDate | 2024-04-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj.art-c4730e1084f441afa52944c064513d3b2024-04-02T01:27:22ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182024-04-011841057106710.3778/j.issn.1673-9418.2302077Multi-feature Interaction for Aspect Sentiment Triplet ExtractionCHEN Linying, LIU Jianhua, ZHENG Zhixiong, LIN Jie, XU Ge, SUN Shuihua01. School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China 2. Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fuzhou 350118, China 3. College of Computer and Control Engineering, Minjiang University, Fuzhou 350108, ChinaAspect sentiment triple extraction is one of the subtasks of aspect-level sentiment analysis, which aims to extract aspect terms, corresponding opinion terms and sentiment polarity in sentence. Previous studies focus on designing a new paradigm to complete the triplet extraction task in an end-to-end manner. They ignore the role of external knowledge in the model, thus semantic information, part-of-speech information and local context information are not fully explored and utilized. Aiming at the above problems, multi-feature interaction for aspect sentiment triplet extraction (MFI-ASTE) is proposed. Firstly, the bidirectional encoder representation from transformers (BERT) model is used to learn the context semantic feature information, meanwhile, the self-attention mechanism is used to strengthen the semantic feature. Secondly, the semantic feature interacts with the extracted part-of-speech feature and both learn from each other to strengthen the combination ability of the part-of-speech and semantic information. Thirdly, many convolutional neural networks are used to extract multiple local context features of each word, and then multi-point gate mechanism is used to filter these features. Fourthly, three features of external knowledge are fused by two linear layers. Finally, biaffine attention is used for predicting grid tagging and specific decoding schemes are used for decoding triplets. Experimental results show that the proposed model improves the F1 score by 6.83%, 5.60%, 0.54% and 1.22% respectively on four datasets compared with existing mainstream models.http://fcst.ceaj.org/fileup/1673-9418/PDF/2302077 .pdfaspect sentiment triplet extraction; self-attention mechanism; convolutional neural network; grid tagging scheme; biaffine attention mechanism |
| spellingShingle | CHEN Linying, LIU Jianhua, ZHENG Zhixiong, LIN Jie, XU Ge, SUN Shuihua Multi-feature Interaction for Aspect Sentiment Triplet Extraction Jisuanji kexue yu tansuo aspect sentiment triplet extraction; self-attention mechanism; convolutional neural network; grid tagging scheme; biaffine attention mechanism |
| title | Multi-feature Interaction for Aspect Sentiment Triplet Extraction |
| title_full | Multi-feature Interaction for Aspect Sentiment Triplet Extraction |
| title_fullStr | Multi-feature Interaction for Aspect Sentiment Triplet Extraction |
| title_full_unstemmed | Multi-feature Interaction for Aspect Sentiment Triplet Extraction |
| title_short | Multi-feature Interaction for Aspect Sentiment Triplet Extraction |
| title_sort | multi feature interaction for aspect sentiment triplet extraction |
| topic | aspect sentiment triplet extraction; self-attention mechanism; convolutional neural network; grid tagging scheme; biaffine attention mechanism |
| url | http://fcst.ceaj.org/fileup/1673-9418/PDF/2302077 .pdf |
| work_keys_str_mv | AT chenlinyingliujianhuazhengzhixionglinjiexugesunshuihua multifeatureinteractionforaspectsentimenttripletextraction |