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        <title>Comparative Opinion Quintuple Extraction from Product Reviews</title>
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    <name type="personal">
        <namePart type="given">Ziheng</namePart>
        <namePart type="family">Liu</namePart>
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    <name type="personal">
        <namePart type="given">Rui</namePart>
        <namePart type="family">Xia</namePart>
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    <name type="personal">
        <namePart type="given">Jianfei</namePart>
        <namePart type="family">Yu</namePart>
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        <dateIssued>2021-nov</dateIssued>
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            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
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    <abstract>As an important task in opinion mining, comparative opinion mining aims to identify comparative sentences from product reviews, extract the comparative elements, and obtain the corresponding comparative opinion tuples. However, most previous studies simply regarded comparative tuple extraction as comparative element extraction, but ignored the fact that many comparative sentences may contain multiple comparisons. The comparative opinion tuples defined in these studies also failed to explicitly provide comparative preferences. To address these limitations, in this work we first introduce a new Comparative Opinion Quintuple Extraction (COQE) task, to identify comparative sentences from product reviews and extract all comparative opinion quintuples (Subject, Object, Comparative Aspect, Comparative Opinion, Comparative Preference). Secondly, based on the existing comparative opinion mining corpora, we make supplementary annotations and construct three datasets for the COQE task. Finally, we benchmark the COQE task by proposing a new BERT-based multi-stage approach as well as three baseline systems extended from previous methods. %The new approach significantly outperforms three baseline systems on three datasets and represents a strong benchmark for COQE. Experimental results show that the new approach significantly outperforms three baseline systems on three datasets for the COQE task.</abstract>
    <identifier type="citekey">liu-etal-2021-comparative</identifier>
    <identifier type="doi">10.18653/v1/2021.emnlp-main.322</identifier>
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        <url>https://aclanthology.org/2021.emnlp-main.322</url>
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        <date>2021-nov</date>
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            <start>3955</start>
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%0 Conference Proceedings
%T Comparative Opinion Quintuple Extraction from Product Reviews
%A Liu, Ziheng
%A Xia, Rui
%A Yu, Jianfei
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 nov
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F liu-etal-2021-comparative
%X As an important task in opinion mining, comparative opinion mining aims to identify comparative sentences from product reviews, extract the comparative elements, and obtain the corresponding comparative opinion tuples. However, most previous studies simply regarded comparative tuple extraction as comparative element extraction, but ignored the fact that many comparative sentences may contain multiple comparisons. The comparative opinion tuples defined in these studies also failed to explicitly provide comparative preferences. To address these limitations, in this work we first introduce a new Comparative Opinion Quintuple Extraction (COQE) task, to identify comparative sentences from product reviews and extract all comparative opinion quintuples (Subject, Object, Comparative Aspect, Comparative Opinion, Comparative Preference). Secondly, based on the existing comparative opinion mining corpora, we make supplementary annotations and construct three datasets for the COQE task. Finally, we benchmark the COQE task by proposing a new BERT-based multi-stage approach as well as three baseline systems extended from previous methods. %The new approach significantly outperforms three baseline systems on three datasets and represents a strong benchmark for COQE. Experimental results show that the new approach significantly outperforms three baseline systems on three datasets for the COQE task.
%R 10.18653/v1/2021.emnlp-main.322
%U https://aclanthology.org/2021.emnlp-main.322
%U https://doi.org/10.18653/v1/2021.emnlp-main.322
%P 3955-3965

Markdown (Informal)

[Comparative Opinion Quintuple Extraction from Product Reviews](https://aclanthology.org/2021.emnlp-main.322) (Liu et al., EMNLP 2021)

• Comparative Opinion Quintuple Extraction from Product Reviews (Liu et al., EMNLP 2021)

ACL

• Ziheng Liu, Rui Xia, and Jianfei Yu. 2021. Comparative Opinion Quintuple Extraction from Product Reviews. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3955–3965, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.