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    <titleInfo>
        <title>Word Relation Autoencoder for Unseen Hypernym Extraction Using Word Embeddings</title>
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    <name type="personal">
        <namePart type="given">Hong-You</namePart>
        <namePart type="family">Chen</namePart>
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    <name type="personal">
        <namePart type="given">Cheng-Syuan</namePart>
        <namePart type="family">Lee</namePart>
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    <name type="personal">
        <namePart type="given">Keng-Te</namePart>
        <namePart type="family">Liao</namePart>
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    <name type="personal">
        <namePart type="given">Shou-De</namePart>
        <namePart type="family">Lin</namePart>
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        <dateIssued>2018-oct-nov</dateIssued>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
            <namePart type="family">Riloff</namePart>
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        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Chiang</namePart>
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        <name type="personal">
            <namePart type="given">Jun’ichi</namePart>
            <namePart type="family">Tsujii</namePart>
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    <abstract>Lexicon relation extraction given distributional representation of words is an important topic in NLP. We observe that the state-of-the-art projection-based methods cannot be generalized to handle unseen hypernyms. We propose to analyze it in the perspective of pollution and construct the corresponding indicator to measure it. We propose a word relation autoencoder (WRAE) model to address the challenge. Experiments on several hypernym-like lexicon datasets show that our model outperforms the competitors significantly.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1519</identifier>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T Word Relation Autoencoder for Unseen Hypernym Extraction Using Word Embeddings
%A Chen, Hong-You
%A Lee, Cheng-Syuan
%A Liao, Keng-Te
%A Lin, Shou-De
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F chen-etal-2018-word
%X Lexicon relation extraction given distributional representation of words is an important topic in NLP. We observe that the state-of-the-art projection-based methods cannot be generalized to handle unseen hypernyms. We propose to analyze it in the perspective of pollution and construct the corresponding indicator to measure it. We propose a word relation autoencoder (WRAE) model to address the challenge. Experiments on several hypernym-like lexicon datasets show that our model outperforms the competitors significantly.
%R 10.18653/v1/D18-1519
%U https://aclanthology.org/D18-1519/
%U https://doi.org/10.18653/v1/D18-1519
%P 4834-4839

Markdown (Informal)

[Word Relation Autoencoder for Unseen Hypernym Extraction Using Word Embeddings](https://aclanthology.org/D18-1519/) (Chen et al., EMNLP 2018)

• Word Relation Autoencoder for Unseen Hypernym Extraction Using Word Embeddings (Chen et al., EMNLP 2018)

ACL

• Hong-You Chen, Cheng-Syuan Lee, Keng-Te Liao, and Shou-De Lin. 2018. Word Relation Autoencoder for Unseen Hypernym Extraction Using Word Embeddings. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4834–4839, Brussels, Belgium. Association for Computational Linguistics.