@inproceedings{bingfei-etal-2023-learnable,
title = "Learnable Conjunction Enhanced Model for {C}hinese Sentiment Analysis",
author = "Bingfei, Zhao and
Hongying, Zan and
Jiajia, Wang and
Yingjie, Han",
editor = "Sun, Maosong and
Qin, Bing and
Qiu, Xipeng and
Jiang, Jing and
Han, Xianpei",
booktitle = "Proceedings of the 22nd Chinese National Conference on Computational Linguistics",
month = aug,
year = "2023",
address = "Harbin, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2023.ccl-1.65",
pages = "761--772",
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{''}",
language = "English",
}
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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)
ACL