Fighting with the Sparsity of Synonymy Dictionaries for Automatic Synset Induction

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10716)


Graph-based synset induction methods, such as MaxMax and Watset, induce synsets by performing a global clustering of a synonymy graph. However, such methods are sensitive to the structure of the input synonymy graph: sparseness of the input dictionary can substantially reduce the quality of the extracted synsets. In this paper, we propose two different approaches designed to alleviate the incompleteness of the input dictionaries. The first one performs a pre-processing of the graph by adding missing edges, while the second one performs a post-processing by merging similar synset clusters. We evaluate these approaches on two datasets for the Russian language and discuss their impact on the performance of synset induction methods. Finally, we perform an extensive error analysis of each approach and discuss prominent alternative methods for coping with the problem of sparsity of the synonymy dictionaries.


Lexical semantics Word embeddings Synset induction Synonyms Word sense induction Synset induction Sense embeddings 



We acknowledge the support of the Deutsche Forschungsgemeinschaft (DFG) under the “JOIN-T” project, the DAAD, the RFBR under the projects no. 16-37-00203 Open image in new window and no. 16-37-00354 Open image in new window and the RFH under the project no. 16-04-12019. The research was supported by the Ministry of Education and Science of the Russian Federation Agreement no. 02.A03.21.0006. The calculations were carried out using the supercomputer “Uran” at the Krasovskii Institute of Mathematics and Mechanics. Finally, we also thank four anonymous reviewers for their helpful comments.


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Authors and Affiliations

  1. 1.Ural Federal UniversityYekaterinburgRussia
  2. 2.Krasovskii Institute of Mathematics and MechanicsYekaterinburgRussia
  3. 3.Universität HamburgHamburgGermany

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