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        <title>Implementing a Reverse Dictionary, based on word definitions, using a Node-Graph Architecture</title>
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        <namePart type="given">Sushrut</namePart>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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    <abstract>In this paper, we outline an approach to build graph-based reverse dictionaries using word definitions. A reverse dictionary takes a phrase as an input and outputs a list of words semantically similar to that phrase. It is a solution to the Tip-of-the-Tongue problem. We use a distance-based similarity measure, computed on a graph, to assess the similarity between a word and the input phrase. We compare the performance of our approach with the Onelook Reverse Dictionary and a distributional semantics method based on word2vec, and show that our approach is much better than the distributional semantics method, and as good as Onelook, on a 3k lexicon. This simple approach sets a new performance baseline for reverse dictionaries.</abstract>
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%0 Conference Proceedings
%T Implementing a Reverse Dictionary, based on word definitions, using a Node-Graph Architecture
%A Thorat, Sushrut
%A Choudhari, Varad
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F thorat-choudhari-2016-implementing
%X In this paper, we outline an approach to build graph-based reverse dictionaries using word definitions. A reverse dictionary takes a phrase as an input and outputs a list of words semantically similar to that phrase. It is a solution to the Tip-of-the-Tongue problem. We use a distance-based similarity measure, computed on a graph, to assess the similarity between a word and the input phrase. We compare the performance of our approach with the Onelook Reverse Dictionary and a distributional semantics method based on word2vec, and show that our approach is much better than the distributional semantics method, and as good as Onelook, on a 3k lexicon. This simple approach sets a new performance baseline for reverse dictionaries.
%U https://aclanthology.org/C16-1263
%P 2797-2806

Markdown (Informal)

[Implementing a Reverse Dictionary, based on word definitions, using a Node-Graph Architecture](https://aclanthology.org/C16-1263) (Thorat & Choudhari, COLING 2016)

• Implementing a Reverse Dictionary, based on word definitions, using a Node-Graph Architecture (Thorat & Choudhari, COLING 2016)

ACL

• Sushrut Thorat and Varad Choudhari. 2016. Implementing a Reverse Dictionary, based on word definitions, using a Node-Graph Architecture. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2797–2806, Osaka, Japan. The COLING 2016 Organizing Committee.