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        <title>An Artificial Language Evaluation of Distributional Semantic Models</title>
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        <namePart type="given">Fatemeh</namePart>
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        <dateIssued>2017-08</dateIssued>
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            <title>Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)</title>
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            <namePart type="given">Roger</namePart>
            <namePart type="family">Levy</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
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    <abstract>Recent studies of distributional semantic models have set up a competition between word embeddings obtained from predictive neural networks and word vectors obtained from abstractive count-based models. This paper is an attempt to reveal the underlying contribution of additional training data and post-processing steps on each type of model in word similarity and relatedness inference tasks. We do so by designing an artificial language framework, training a predictive and a count-based model on data sampled from this grammar, and evaluating the resulting word vectors in paradigmatic and syntagmatic tasks defined with respect to the grammar.</abstract>
    <identifier type="citekey">torabi-asr-jones-2017-artificial</identifier>
    <identifier type="doi">10.18653/v1/K17-1015</identifier>
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        <date>2017-08</date>
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%0 Conference Proceedings
%T An Artificial Language Evaluation of Distributional Semantic Models
%A Torabi Asr, Fatemeh
%A Jones, Michael
%Y Levy, Roger
%Y Specia, Lucia
%S Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada
%F torabi-asr-jones-2017-artificial
%X Recent studies of distributional semantic models have set up a competition between word embeddings obtained from predictive neural networks and word vectors obtained from abstractive count-based models. This paper is an attempt to reveal the underlying contribution of additional training data and post-processing steps on each type of model in word similarity and relatedness inference tasks. We do so by designing an artificial language framework, training a predictive and a count-based model on data sampled from this grammar, and evaluating the resulting word vectors in paradigmatic and syntagmatic tasks defined with respect to the grammar.
%R 10.18653/v1/K17-1015
%U https://aclanthology.org/K17-1015
%U https://doi.org/10.18653/v1/K17-1015
%P 134-142

Markdown (Informal)

[An Artificial Language Evaluation of Distributional Semantic Models](https://aclanthology.org/K17-1015) (Torabi Asr & Jones, CoNLL 2017)

• An Artificial Language Evaluation of Distributional Semantic Models (Torabi Asr & Jones, CoNLL 2017)

ACL

• Fatemeh Torabi Asr and Michael Jones. 2017. An Artificial Language Evaluation of Distributional Semantic Models. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), pages 134–142, Vancouver, Canada. Association for Computational Linguistics.