Conference article

Using Pseudowords for Algorithm Comparison: An Evaluation Framework for Graph-based Word Sense Induction

Flavio Massimiliano Cecchini
DISCo, Universit`a degli Studi di Milano-Bicocca, Italy

Martin Riedl
Language Technology Group, Universität Hamburg, Germany

Chris Biemann
Language Technology Group, Universität Hamburg, Germany

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Published in: Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017, Gothenburg, Sweden

Linköping Electronic Conference Proceedings 131:13, p. 105-114

NEALT Proceedings Series 29:13, p. 105-114

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Published: 2017-05-08

ISBN: 978-91-7685-601-7

ISSN: 1650-3686 (print), 1650-3740 (online)


In this paper we define two parallel data sets based on pseudowords, extracted from the same corpus. They both consist of word-centered graphs for each of 1225 different pseudowords, and use respectively first-order co-occurrences and secondorder semantic similarities. We propose an evaluation framework on these data sets for graph-based Word Sense Induction (WSI) focused on the case of coarsegrained homonymy: We compare different WSI clustering algorithms by measuring how well their outputs agree with the a priori known ground-truth decomposition of a pseudoword. We perform this evaluation for four different clustering algorithms: the Markov cluster algorithm, Chinese Whispers, MaxMax and a gangplankbased clustering algorithm. To further improve the comparison between these algorithms and the analysis of their behaviours, we also define a new specific evaluation measure. As far as we know, this is the first large-scale systematic pseudoword evaluation dedicated to the induction of coarsegrained homonymous word senses.


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