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        <title>Learnable Conjunction Enhanced Model for Chinese Sentiment Analysis</title>
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        <namePart type="given">Zhao</namePart>
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        <namePart type="family">Hongying</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Han</namePart>
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            <title>Proceedings of the 22nd Chinese National Conference on Computational Linguistics</title>
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            <namePart type="given">Maosong</namePart>
            <namePart type="family">Sun</namePart>
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            <publisher>Chinese Information Processing Society of China</publisher>
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    <abstract>“Sentiment analysis is a crucial text classification task that aims to extract, process, and analyzeopinions, sentiments, and subjectivity within texts. In current research on Chinese text, sentenceand aspect-based sentiment analysis is mainly tackled through well-designed models. However,despite the importance of word order and function words as essential means of semantic ex-pression in Chinese, they are often underutilized. This paper presents a new Chinese sentimentanalysis method that utilizes a Learnable Conjunctions Enhanced Model (LCEM). The LCEMadjusts the general structure of the pre-trained language model and incorporates conjunctionslocation information into the model’s fine-tuning process. Additionally, we discuss a variantstructure of residual connections to construct a residual structure that can learn critical informa-tion in the text and optimize it during training. We perform experiments on the public datasetsand demonstrate that our approach enhances performance on both sentence and aspect-basedsentiment analysis datasets compared to the baseline pre-trained language models. These resultsconfirm the effectiveness of our proposed method. Introduction”</abstract>
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%0 Conference Proceedings
%T Learnable Conjunction Enhanced Model for Chinese Sentiment Analysis
%A Bingfei, Zhao
%A Hongying, Zan
%A Jiajia, Wang
%A Yingjie, Han
%Y Sun, Maosong
%Y Qin, Bing
%Y Qiu, Xipeng
%Y Jiang, Jing
%Y Han, Xianpei
%S Proceedings of the 22nd Chinese National Conference on Computational Linguistics
%D 2023
%8 August
%I Chinese Information Processing Society of China
%C Harbin, China
%G English
%F bingfei-etal-2023-learnable
%X “Sentiment analysis is a crucial text classification task that aims to extract, process, and analyzeopinions, sentiments, and subjectivity within texts. In current research on Chinese text, sentenceand aspect-based sentiment analysis is mainly tackled through well-designed models. However,despite the importance of word order and function words as essential means of semantic ex-pression in Chinese, they are often underutilized. This paper presents a new Chinese sentimentanalysis method that utilizes a Learnable Conjunctions Enhanced Model (LCEM). The LCEMadjusts the general structure of the pre-trained language model and incorporates conjunctionslocation information into the model’s fine-tuning process. Additionally, we discuss a variantstructure of residual connections to construct a residual structure that can learn critical informa-tion in the text and optimize it during training. We perform experiments on the public datasetsand demonstrate that our approach enhances performance on both sentence and aspect-basedsentiment analysis datasets compared to the baseline pre-trained language models. These resultsconfirm the effectiveness of our proposed method. Introduction”
%U https://aclanthology.org/2023.ccl-1.65
%P 761-772

Markdown (Informal)

[Learnable Conjunction Enhanced Model for Chinese Sentiment Analysis](https://aclanthology.org/2023.ccl-1.65) (Bingfei et al., CCL 2023)

• Learnable Conjunction Enhanced Model for Chinese Sentiment Analysis (Bingfei et al., CCL 2023)

ACL

• Zhao Bingfei, Zan Hongying, Wang Jiajia, and Han Yingjie. 2023. Learnable Conjunction Enhanced Model for Chinese Sentiment Analysis. In Proceedings of the 22nd Chinese National Conference on Computational Linguistics, pages 761–772, Harbin, China. Chinese Information Processing Society of China.