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| Span-level aspect sentiment triplet extraction based on curriculum learning |
Mingze HOU( ),Lei RAO*(),Guangyu FAN,Niansheng CHEN,Songlin CHENG |
| School of Electronic Information Engineering Shanghai Dianji University, Shanghai 201306, China |
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Abstract Exiting methods of aspect sentiment triplet extraction suffer from the problems of not being able to fully utilize the knowledge of the pre-trained model, being prone to overfitting or underfitting, and having insufficient ability to recognize the fine-grained aspects and sentiments of an utterance. A method for extracting span-level aspect sentiment triples based on a curriculum learning framework was proposed. Data preprocessing was performed based on the curriculum learning framework, and the contextual representation of a sentence was learned using a pre-trained model. By building a span model, all possible spans were extracted in a sentence. Aspect and opinion terms were extracted based on the dual channel, and the correct combinations of aspect-opinion were filtered out for sentiment categorization. Experimental results on the ASTE-Data-V2 dataset show that the F1 value of the proposed method is improved by 2 percentage points over that of SPAN-ASTE. The experimental results of the proposed method outperform the other aspect sentiment triplet extraction methods such as GTS, B-MRC, and JET.
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Received: 19 November 2023
Published: 18 January 2025
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| Fund: 国家自然科学基金资助项目（61702320）. |
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Corresponding Authors:
Lei RAO
E-mail: 226003010119@st.sdju.edu.cn;raol@sdju.edu.cn
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基于课程学习的跨度级方面情感三元组提取
现有方面情感三元组提取方法存在无法充分利用预训练模型知识，容易出现过拟合或欠拟合，识别语句细粒度方面词和情感极性的能力不足等问题，为此提出基于课程学习框架的跨度级方面情感三元组提取方法. 该方法基于课程学习框架进行数据预处理，使用预训练模型学习句子的上下文表示，搭建跨度模型提取句子中所有可能的跨度，基于双通道提取方面词和意见词，筛出正确的方面词和意见词组合进行情感分类. 在ASTE-Data-V2数据集上的实验结果表明，所提方法的F1值比SPAN-ASTE的F1值提升了2个百分点，所提方法的实验结果优于GTS、B-MRC、JET等其他方面情感三元组提取方法.
关键词：
课程学习,
跨度模型,
方面情感三元组提取,
双通道,
情感分类
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