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    <titleInfo>
        <title>Neural Attentive Bag-of-Entities Model for Text Classification</title>
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    <name type="personal">
        <namePart type="given">Ikuya</namePart>
        <namePart type="family">Yamada</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Hiroyuki</namePart>
        <namePart type="family">Shindo</namePart>
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        <dateIssued>2019-11</dateIssued>
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            <title>Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)</title>
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        <name type="personal">
            <namePart type="given">Mohit</namePart>
            <namePart type="family">Bansal</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Aline</namePart>
            <namePart type="family">Villavicencio</namePart>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>This study proposes a Neural Attentive Bag-of-Entities model, which is a neural network model that performs text classification using entities in a knowledge base. Entities provide unambiguous and relevant semantic signals that are beneficial for text classification. We combine simple high-recall entity detection based on a dictionary, to detect entities in a document, with a novel neural attention mechanism that enables the model to focus on a small number of unambiguous and relevant entities. We tested the effectiveness of our model using two standard text classification datasets (i.e., the 20 Newsgroups and R8 datasets) and a popular factoid question answering dataset based on a trivia quiz game. As a result, our model achieved state-of-the-art results on all datasets. The source code of the proposed model is available online at https://github.com/wikipedia2vec/wikipedia2vec.</abstract>
    <identifier type="citekey">yamada-shindo-2019-neural</identifier>
    <identifier type="doi">10.18653/v1/K19-1052</identifier>
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        <url>https://aclanthology.org/K19-1052</url>
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        <date>2019-11</date>
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%0 Conference Proceedings
%T Neural Attentive Bag-of-Entities Model for Text Classification
%A Yamada, Ikuya
%A Shindo, Hiroyuki
%Y Bansal, Mohit
%Y Villavicencio, Aline
%S Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F yamada-shindo-2019-neural
%X This study proposes a Neural Attentive Bag-of-Entities model, which is a neural network model that performs text classification using entities in a knowledge base. Entities provide unambiguous and relevant semantic signals that are beneficial for text classification. We combine simple high-recall entity detection based on a dictionary, to detect entities in a document, with a novel neural attention mechanism that enables the model to focus on a small number of unambiguous and relevant entities. We tested the effectiveness of our model using two standard text classification datasets (i.e., the 20 Newsgroups and R8 datasets) and a popular factoid question answering dataset based on a trivia quiz game. As a result, our model achieved state-of-the-art results on all datasets. The source code of the proposed model is available online at https://github.com/wikipedia2vec/wikipedia2vec.
%R 10.18653/v1/K19-1052
%U https://aclanthology.org/K19-1052
%U https://doi.org/10.18653/v1/K19-1052
%P 563-573

Markdown (Informal)

[Neural Attentive Bag-of-Entities Model for Text Classification](https://aclanthology.org/K19-1052) (Yamada & Shindo, CoNLL 2019)

• Neural Attentive Bag-of-Entities Model for Text Classification (Yamada & Shindo, CoNLL 2019)

ACL

• Ikuya Yamada and Hiroyuki Shindo. 2019. Neural Attentive Bag-of-Entities Model for Text Classification. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 563–573, Hong Kong, China. Association for Computational Linguistics.