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    <titleInfo>
        <title>Target-Guided Structured Attention Network for Target-Dependent Sentiment Analysis</title>
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    <name type="personal">
        <namePart type="given">Ji</namePart>
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    <name type="personal">
        <namePart type="given">Chengyao</namePart>
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    <name type="personal">
        <namePart type="given">Pengfei</namePart>
        <namePart type="family">Liu</namePart>
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    <name type="personal">
        <namePart type="given">Chao</namePart>
        <namePart type="family">He</namePart>
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    <name type="personal">
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        <namePart type="given">Wing-Ki</namePart>
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    <abstract>Target-dependent sentiment analysis (TDSA) aims to classify the sentiment of a text towards a given target. The major challenge of this task lies in modeling the semantic relatedness between a target and its context sentence. This paper proposes a novel Target-Guided Structured Attention Network (TG-SAN), which captures target-related contexts for TDSA in a fine-to-coarse manner. Given a target and its context sentence, the proposed TG-SAN first identifies multiple semantic segments from the sentence using a target-guided structured attention mechanism. It then fuses the extracted segments based on their relatedness with the target for sentiment classification. We present comprehensive comparative experiments on three benchmarks with three major findings. First, TG-SAN outperforms the state-of-the-art by up to 1.61% and 3.58% in terms of accuracy and Marco-F1, respectively. Second, it shows a strong advantage in determining the sentiment of a target when the context sentence contains multiple semantic segments. Lastly, visualization results show that the attention scores produced by TG-SAN are highly interpretable</abstract>
    <identifier type="citekey">zhang-etal-2020-target</identifier>
    <identifier type="doi">10.1162/tacl_a_00308</identifier>
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        <url>https://aclanthology.org/2020.tacl-1.12</url>
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        <date>2020</date>
        <detail type="volume"><number>8</number></detail>
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            <start>172</start>
            <end>182</end>
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%0 Journal Article
%T Target-Guided Structured Attention Network for Target-Dependent Sentiment Analysis
%A Zhang, Ji
%A Chen, Chengyao
%A Liu, Pengfei
%A He, Chao
%A Leung, Cane Wing-Ki
%J Transactions of the Association for Computational Linguistics
%D 2020
%V 8
%I MIT Press
%C Cambridge, MA
%F zhang-etal-2020-target
%X Target-dependent sentiment analysis (TDSA) aims to classify the sentiment of a text towards a given target. The major challenge of this task lies in modeling the semantic relatedness between a target and its context sentence. This paper proposes a novel Target-Guided Structured Attention Network (TG-SAN), which captures target-related contexts for TDSA in a fine-to-coarse manner. Given a target and its context sentence, the proposed TG-SAN first identifies multiple semantic segments from the sentence using a target-guided structured attention mechanism. It then fuses the extracted segments based on their relatedness with the target for sentiment classification. We present comprehensive comparative experiments on three benchmarks with three major findings. First, TG-SAN outperforms the state-of-the-art by up to 1.61% and 3.58% in terms of accuracy and Marco-F1, respectively. Second, it shows a strong advantage in determining the sentiment of a target when the context sentence contains multiple semantic segments. Lastly, visualization results show that the attention scores produced by TG-SAN are highly interpretable
%R 10.1162/tacl_a_00308
%U https://aclanthology.org/2020.tacl-1.12
%U https://doi.org/10.1162/tacl_a_00308
%P 172-182

Markdown (Informal)

[Target-Guided Structured Attention Network for Target-Dependent Sentiment Analysis](https://aclanthology.org/2020.tacl-1.12) (Zhang et al., TACL 2020)

• Target-Guided Structured Attention Network for Target-Dependent Sentiment Analysis (Zhang et al., TACL 2020)

ACL

• Ji Zhang, Chengyao Chen, Pengfei Liu, Chao He, and Cane Wing-Ki Leung. 2020. Target-Guided Structured Attention Network for Target-Dependent Sentiment Analysis. Transactions of the Association for Computational Linguistics, 8:172–182.