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        <title>A Computational Study on Word Meanings and Their Distributed Representations via Polymodal Embedding</title>
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        <namePart type="given">Joohee</namePart>
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            <namePart type="given">Greg</namePart>
            <namePart type="family">Kondrak</namePart>
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    <abstract>A distributed representation has become a popular approach to capturing a word meaning. Besides its success and practical value, however, questions arise about the relationships between a true word meaning and its distributed representation. In this paper, we examine such a relationship via polymodal embedding approach inspired by the theory that humans tend to use diverse sources in developing a word meaning. The result suggests that the existing embeddings lack in capturing certain aspects of word meanings which can be significantly improved by the polymodal approach. Also, we show distinct characteristics of different types of words (e.g. concreteness) via computational studies. Finally, we show our proposed embedding method outperforms the baselines in the word similarity measure tasks and the hypernym prediction tasks.</abstract>
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%0 Conference Proceedings
%T A Computational Study on Word Meanings and Their Distributed Representations via Polymodal Embedding
%A Park, Joohee
%A Myaeng, Sung-hyon
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F park-myaeng-2017-computational
%X A distributed representation has become a popular approach to capturing a word meaning. Besides its success and practical value, however, questions arise about the relationships between a true word meaning and its distributed representation. In this paper, we examine such a relationship via polymodal embedding approach inspired by the theory that humans tend to use diverse sources in developing a word meaning. The result suggests that the existing embeddings lack in capturing certain aspects of word meanings which can be significantly improved by the polymodal approach. Also, we show distinct characteristics of different types of words (e.g. concreteness) via computational studies. Finally, we show our proposed embedding method outperforms the baselines in the word similarity measure tasks and the hypernym prediction tasks.
%U https://aclanthology.org/I17-1022
%P 214-223

Markdown (Informal)

[A Computational Study on Word Meanings and Their Distributed Representations via Polymodal Embedding](https://aclanthology.org/I17-1022) (Park & Myaeng, IJCNLP 2017)

• A Computational Study on Word Meanings and Their Distributed Representations via Polymodal Embedding (Park & Myaeng, IJCNLP 2017)

ACL

• Joohee Park and Sung-hyon Myaeng. 2017. A Computational Study on Word Meanings and Their Distributed Representations via Polymodal Embedding. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 214–223, Taipei, Taiwan. Asian Federation of Natural Language Processing.