Fighting with the sparsity of the synonymy dictionaries for automatic synset induction

Ustalov, Dmitry ; Chernoskutov, Mikhail ; Panchenko, Alexander ; Biemann, Chris

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Document Type: Conference or workshop publication
Year of publication: 2018
Book title: Analysis of Images, Social Networks and Texts : 6th International Conference, AIST 2017, Moscow, Russia, July 27-29, 2017, Revised Selected Papers
The title of a journal, publication series: Lecture Notes in Computer Science
Volume: 10716
Page range: 94-105
Conference title: Analysis of Images, Social Networks and Texts, AIST 2017
Location of the conference venue: Moscow, Russia
Date of the conference: July 27-29, 2017
Publisher: Aalst, Wil M. P. van der
Place of publication: Berlin [u.a.]
Publishing house: Springer
ISBN: 978-3-319-73012-7 , 978-3-319-73013-4
ISSN: 0302-9743 , 1611-3349
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Publication language: Other
Institution: School of Business Informatics and Mathematics > Information Systems III: Enterprise Data Analysis (Ponzetto 2016-)
Subject: 004 Computer science, internet
Keywords (English): lexical semantics , word embeddings , synset induction , synonyms , word sense induction , synset induction , sense embeddings
Abstract: 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.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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