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        <title>A Cross-Topic Method for Supervised Relevance Classification</title>
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    <name type="personal">
        <namePart type="given">Jiawei</namePart>
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            <title>Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)</title>
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        <name type="personal">
            <namePart type="given">Wei</namePart>
            <namePart type="family">Xu</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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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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            <namePart type="given">Afshin</namePart>
            <namePart type="family">Rahimi</namePart>
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    <abstract>In relevance classification, we hope to judge whether some utterances expressed on a topic are relevant or not. A usual method is to train a specific classifier respectively for each topic. However, in that way, it easily causes an underfitting problem in supervised learning model, since annotated data can be insufficient for every single topic. In this paper, we explore the common features beyond different topics and propose our cross-topic relevance embedding aggregation methodology (CREAM) that can expand the range of training data and apply what has been learned from source topics to a target topic. In our experiment, we show that our proposal could capture common features within a small amount of annotated data and improve the performance of relevance classification compared with other baselines.</abstract>
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    <identifier type="doi">10.18653/v1/D19-5520</identifier>
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        <date>2019-11</date>
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%0 Conference Proceedings
%T A Cross-Topic Method for Supervised Relevance Classification
%A Yong, Jiawei
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F yong-2019-cross
%X In relevance classification, we hope to judge whether some utterances expressed on a topic are relevant or not. A usual method is to train a specific classifier respectively for each topic. However, in that way, it easily causes an underfitting problem in supervised learning model, since annotated data can be insufficient for every single topic. In this paper, we explore the common features beyond different topics and propose our cross-topic relevance embedding aggregation methodology (CREAM) that can expand the range of training data and apply what has been learned from source topics to a target topic. In our experiment, we show that our proposal could capture common features within a small amount of annotated data and improve the performance of relevance classification compared with other baselines.
%R 10.18653/v1/D19-5520
%U https://aclanthology.org/D19-5520
%U https://doi.org/10.18653/v1/D19-5520
%P 147-152

Markdown (Informal)

[A Cross-Topic Method for Supervised Relevance Classification](https://aclanthology.org/D19-5520) (Yong, WNUT 2019)

• A Cross-Topic Method for Supervised Relevance Classification (Yong, WNUT 2019)

ACL

• Jiawei Yong. 2019. A Cross-Topic Method for Supervised Relevance Classification. In Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), pages 147–152, Hong Kong, China. Association for Computational Linguistics.