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| Autori principali: | , , , |
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| Natura: | Preprint |
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2023
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| Accesso online: | https://arxiv.org/abs/2311.03896 |
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| _version_ | 1866913400477450240 |
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| author | Xu, Xiancai Zhang, Jia-Dong Xiong, Lei Liu, Zhishang |
| author_facet | Xu, Xiancai Zhang, Jia-Dong Xiong, Lei Liu, Zhishang |
| contents | Aspect-based sentiment analysis (ABSA) have been extensively studied, but little light has been shed on the quadruple extraction consisting of four fundamental elements: aspects, categories, opinions and sentiments, especially with implicit aspects and opinions. In this paper, we propose a new method iACOS for extracting Implicit Aspects with Categories and Opinions with Sentiments. First, iACOS appends two implicit tokens at the end of a text to capture the context-aware representation of all tokens including implicit aspects and opinions. Second, iACOS develops a sequence labeling model over the context-aware token representation to co-extract explicit and implicit aspects and opinions. Third, iACOS devises a multi-label classifier with a specialized multi-head attention for discovering aspect-opinion pairs and predicting their categories and sentiments simultaneously. Fourth, iACOS leverages informative and adaptive negative examples to jointly train the multi-label classifier and the other two classifiers on categories and sentiments by multi-task learning. Finally, the experimental results show that iACOS significantly outperforms other quadruple extraction baselines according to the F1 score on two public benchmark datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_03896 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | iACOS: Advancing Implicit Sentiment Extraction with Informative and Adaptive Negative Examples Xu, Xiancai Zhang, Jia-Dong Xiong, Lei Liu, Zhishang Computation and Language Aspect-based sentiment analysis (ABSA) have been extensively studied, but little light has been shed on the quadruple extraction consisting of four fundamental elements: aspects, categories, opinions and sentiments, especially with implicit aspects and opinions. In this paper, we propose a new method iACOS for extracting Implicit Aspects with Categories and Opinions with Sentiments. First, iACOS appends two implicit tokens at the end of a text to capture the context-aware representation of all tokens including implicit aspects and opinions. Second, iACOS develops a sequence labeling model over the context-aware token representation to co-extract explicit and implicit aspects and opinions. Third, iACOS devises a multi-label classifier with a specialized multi-head attention for discovering aspect-opinion pairs and predicting their categories and sentiments simultaneously. Fourth, iACOS leverages informative and adaptive negative examples to jointly train the multi-label classifier and the other two classifiers on categories and sentiments by multi-task learning. Finally, the experimental results show that iACOS significantly outperforms other quadruple extraction baselines according to the F1 score on two public benchmark datasets. |
| title | iACOS: Advancing Implicit Sentiment Extraction with Informative and Adaptive Negative Examples |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2311.03896 |