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A framework for enriching lexical semantic resources with distributional semantics

Published online by Cambridge University Press:  15 January 2018

CHRIS BIEMANN ,
STEFANO FARALLI ,
ALEXANDER PANCHENKO  and
SIMONE PAOLO PONZETTO
CHRIS BIEMANN
Affiliation:
Language Technology Group, Department of Informatics, Faculty of Mathematics, Informatics, and Natural Sciences, Universität Hamburg, Germany e-mails: biemann@informatik.uni-hamburg.de, panchenko@informatik.uni-hamburg.de
STEFANO FARALLI
Affiliation:
Data and Web Science Group, School of Business Informatics and Mathematics, Universität Mannheim, Germany e-mails: stefano@informatik.uni-mannheim.de, simone@informatik.uni-mannheim.de
ALEXANDER PANCHENKO
Affiliation:
Language Technology Group, Department of Informatics, Faculty of Mathematics, Informatics, and Natural Sciences, Universität Hamburg, Germany e-mails: biemann@informatik.uni-hamburg.de, panchenko@informatik.uni-hamburg.de
SIMONE PAOLO PONZETTO
Affiliation:
Data and Web Science Group, School of Business Informatics and Mathematics, Universität Mannheim, Germany e-mails: stefano@informatik.uni-mannheim.de, simone@informatik.uni-mannheim.de
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Abstract

We present an approach to combining distributional semantic representations induced from text corpora with manually constructed lexical semantic networks. While both kinds of semantic resources are available with high lexical coverage, our aligned resource combines the domain specificity and availability of contextual information from distributional models with the conciseness and high quality of manually crafted lexical networks. We start with a distributional representation of induced senses of vocabulary terms, which are accompanied with rich context information given by related lexical items. We then automatically disambiguate such representations to obtain a full-fledged proto-conceptualization, i.e. a typed graph of induced word senses. In a final step, this proto-conceptualization is aligned to a lexical ontology, resulting in a hybrid aligned resource. Moreover, unmapped induced senses are associated with a semantic type in order to connect them to the core resource. Manual evaluations against ground-truth judgments for different stages of our method as well as an extrinsic evaluation on a knowledge-based Word Sense Disambiguation benchmark all indicate the high quality of the new hybrid resource. Additionally, we show the benefits of enriching top-down lexical knowledge resources with bottom-up distributional information from text for addressing high-end knowledge acquisition tasks such as cleaning hypernym graphs and learning taxonomies from scratch.

Type
Articles
Information
Natural Language Engineering , Volume 24 , Issue 2 , March 2018 , pp. 265 - 312
Copyright
Copyright © Cambridge University Press 2018 

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