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    <titleInfo>
        <title>Learning Distributed Representations of Texts and Entities from Knowledge Base</title>
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    <name type="personal">
        <namePart type="given">Ikuya</namePart>
        <namePart type="family">Yamada</namePart>
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    <name type="personal">
        <namePart type="given">Hiroyuki</namePart>
        <namePart type="family">Shindo</namePart>
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    <name type="personal">
        <namePart type="given">Hideaki</namePart>
        <namePart type="family">Takeda</namePart>
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    <name type="personal">
        <namePart type="given">Yoshiyasu</namePart>
        <namePart type="family">Takefuji</namePart>
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    <abstract>We describe a neural network model that jointly learns distributed representations of texts and knowledge base (KB) entities. Given a text in the KB, we train our proposed model to predict entities that are relevant to the text. Our model is designed to be generic with the ability to address various NLP tasks with ease. We train the model using a large corpus of texts and their entity annotations extracted from Wikipedia. We evaluated the model on three important NLP tasks (i.e., sentence textual similarity, entity linking, and factoid question answering) involving both unsupervised and supervised settings. As a result, we achieved state-of-the-art results on all three of these tasks. Our code and trained models are publicly available for further academic research.</abstract>
    <identifier type="citekey">yamada-etal-2017-learning</identifier>
    <identifier type="doi">10.1162/tacl_a_00069</identifier>
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        <url>https://aclanthology.org/Q17-1028</url>
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        <date>2017</date>
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            <start>397</start>
            <end>411</end>
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%0 Journal Article
%T Learning Distributed Representations of Texts and Entities from Knowledge Base
%A Yamada, Ikuya
%A Shindo, Hiroyuki
%A Takeda, Hideaki
%A Takefuji, Yoshiyasu
%J Transactions of the Association for Computational Linguistics
%D 2017
%V 5
%I MIT Press
%C Cambridge, MA
%F yamada-etal-2017-learning
%X We describe a neural network model that jointly learns distributed representations of texts and knowledge base (KB) entities. Given a text in the KB, we train our proposed model to predict entities that are relevant to the text. Our model is designed to be generic with the ability to address various NLP tasks with ease. We train the model using a large corpus of texts and their entity annotations extracted from Wikipedia. We evaluated the model on three important NLP tasks (i.e., sentence textual similarity, entity linking, and factoid question answering) involving both unsupervised and supervised settings. As a result, we achieved state-of-the-art results on all three of these tasks. Our code and trained models are publicly available for further academic research.
%R 10.1162/tacl_a_00069
%U https://aclanthology.org/Q17-1028
%U https://doi.org/10.1162/tacl_a_00069
%P 397-411

Markdown (Informal)

[Learning Distributed Representations of Texts and Entities from Knowledge Base](https://aclanthology.org/Q17-1028) (Yamada et al., TACL 2017)

• Learning Distributed Representations of Texts and Entities from Knowledge Base (Yamada et al., TACL 2017)

ACL

• Ikuya Yamada, Hiroyuki Shindo, Hideaki Takeda, and Yoshiyasu Takefuji. 2017. Learning Distributed Representations of Texts and Entities from Knowledge Base. Transactions of the Association for Computational Linguistics, 5:397–411.