Semi-supervised methods for expanding psycholinguistics norms by integrating distributional similarity with the structure of WordNet

Michael Mohler, Marc Tomlinson, David Bracewell, Bryan Rink

Abstract
In this work, we present two complementary methods for the expansion of psycholinguistics norms. The first method is a random-traversal spreading activation approach which transfers existing norms onto semantically related terms using notions of synonymy, hypernymy, and pertainymy to approach full coverage of the English language. The second method makes use of recent advances in distributional similarity representation to transfer existing norms to their closest neighbors in a high-dimensional vector space. These two methods (along with a naive hybrid approach combining the two) have been shown to significantly outperform a state-of-the-art resource expansion system at our pilot task of imageability expansion. We have evaluated these systems in a cross-validation experiment using 8,188 norms found in existing pscholinguistics literature. We have also validated the quality of these combined norms by performing a small study using Amazon Mechanical Turk (AMT).
Anthology ID:
L14-1031
Volume:
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
Month:
May
Year:
2014
Address:
Reykjavik, Iceland
Venue:
LREC
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Publisher:
European Language Resources Association (ELRA)
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Pages:
3020–3026
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URL:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/1043_Paper.pdf
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Cite (ACL):
Michael Mohler, Marc Tomlinson, David Bracewell, and Bryan Rink. 2014. Semi-supervised methods for expanding psycholinguistics norms by integrating distributional similarity with the structure of WordNet. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 3020–3026, Reykjavik, Iceland. European Language Resources Association (ELRA).
Cite (Informal):
Semi-supervised methods for expanding psycholinguistics norms by integrating distributional similarity with the structure of WordNet (Mohler et al., LREC 2014)
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http://www.lrec-conf.org/proceedings/lrec2014/pdf/1043_Paper.pdf
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