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Wisdom of crowds versus wisdom of linguists – measuring the semantic relatedness of words

Published online by Cambridge University Press:  09 September 2009

TORSTEN ZESCH  and
IRYNA GUREVYCH
TORSTEN ZESCH
Affiliation:
Ubiquitous Knowledge Processing Lab, Computer Science Department, Technische Universität Darmstadt, Hochschulstr. 10, 64289 Darmstadt, Germany e-mail: [email protected], [email protected]
IRYNA GUREVYCH
Affiliation:
Ubiquitous Knowledge Processing Lab, Computer Science Department, Technische Universität Darmstadt, Hochschulstr. 10, 64289 Darmstadt, Germany e-mail: [email protected], [email protected]
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Abstract

In this article, we present a comprehensive study aimed at computing semantic relatedness of word pairs. We analyze the performance of a large number of semantic relatedness measures proposed in the literature with respect to different experimental conditions, such as (i) the datasets employed, (ii) the language (English or German), (iii) the underlying knowledge source, and (iv) the evaluation task (computing scores of semantic relatedness, ranking word pairs, solving word choice problems). To our knowledge, this study is the first to systematically analyze semantic relatedness on a large number of datasets with different properties, while emphasizing the role of the knowledge source compiled either by the ‘wisdom of linguists’ (i.e., classical wordnets) or by the ‘wisdom of crowds’ (i.e., collaboratively constructed knowledge sources like Wikipedia).

The article discusses benefits and drawbacks of different approaches to evaluating semantic relatedness. We show that results should be interpreted carefully to evaluate particular aspects of semantic relatedness. For the first time, we employ a vector based measure of semantic relatedness, relying on a concept space built from documents, to the first paragraph of Wikipedia articles, to English WordNet glosses, and to GermaNet based pseudo glosses. Contrary to previous research (Strube and Ponzetto 2006; Gabrilovich and Markovitch 2007; Zesch et al. 2007), we find that ‘wisdom of crowds’ based resources are not superior to ‘wisdom of linguists’ based resources. We also find that using the first paragraph of a Wikipedia article as opposed to the whole article leads to better precision, but decreases recall. Finally, we present two systems that were developed to aid the experiments presented herein and are freely available1 for research purposes: (i) DEXTRACT, a software to semi-automatically construct corpus-driven semantic relatedness datasets, and (ii) JWPL, a Java-based high-performance Wikipedia Application Programming Interface (API) for building natural language processing (NLP) applications.

Type
Papers
Information
Natural Language Engineering , Volume 16 , Issue 1 , January 2010 , pp. 25 - 59
Copyright
Copyright © Cambridge University Press 2009

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