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        <title>Distantly Supervised Attribute Detection from Reviews</title>
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    <name type="personal">
        <namePart type="given">Lisheng</namePart>
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            <title>Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text</title>
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        <name type="personal">
            <namePart type="given">Wei</namePart>
            <namePart type="family">Xu</namePart>
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        <name type="personal">
            <namePart type="given">Alan</namePart>
            <namePart type="family">Ritter</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Tim</namePart>
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    <abstract>This work aims to detect specific attributes of a place (e.g., if it has a romantic atmosphere, or if it offers outdoor seating) from its user reviews via distant supervision: without direct annotation of the review text, we use the crowdsourced attribute labels of the place as labels of the review text. We then use review-level attention to pay more attention to those reviews related to the attributes. The experimental results show that our attention-based model predicts attributes for places from reviews with over 98% accuracy. The attention weights assigned to each review provide explanation of capturing relevant reviews.</abstract>
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    <identifier type="doi">10.18653/v1/W18-6110</identifier>
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%0 Conference Proceedings
%T Distantly Supervised Attribute Detection from Reviews
%A Fu, Lisheng
%A Barrio, Pablo
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F fu-barrio-2018-distantly
%X This work aims to detect specific attributes of a place (e.g., if it has a romantic atmosphere, or if it offers outdoor seating) from its user reviews via distant supervision: without direct annotation of the review text, we use the crowdsourced attribute labels of the place as labels of the review text. We then use review-level attention to pay more attention to those reviews related to the attributes. The experimental results show that our attention-based model predicts attributes for places from reviews with over 98% accuracy. The attention weights assigned to each review provide explanation of capturing relevant reviews.
%R 10.18653/v1/W18-6110
%U https://aclanthology.org/W18-6110
%U https://doi.org/10.18653/v1/W18-6110
%P 74-78

Markdown (Informal)

[Distantly Supervised Attribute Detection from Reviews](https://aclanthology.org/W18-6110) (Fu & Barrio, WNUT 2018)

• Distantly Supervised Attribute Detection from Reviews (Fu & Barrio, WNUT 2018)

ACL

• Lisheng Fu and Pablo Barrio. 2018. Distantly Supervised Attribute Detection from Reviews. In Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text, pages 74–78, Brussels, Belgium. Association for Computational Linguistics.