Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/102044
Title: Fighting with the sparsity of synonymy dictionaries for automatic synset induction
Authors: Ustalov, D.
Chernoskutov, M.
Biemann, C.
Panchenko, A.
Issue Date: 2018
Publisher: Springer Verlag
Citation: Fighting with the sparsity of synonymy dictionaries for automatic synset induction / D. Ustalov, M. Chernoskutov, C. Biemann, et al. — DOI 10.1007/978-3-319-73013-4_9 // Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). — 2018. — Vol. 10716 LNCS. — P. 94-105.
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. © Springer International Publishing AG 2018.
Keywords: LEXICAL SEMANTICS
SENSE EMBEDDINGS
SYNONYMS
SYNSET INDUCTION
SYNSET INDUCTION
WORD EMBEDDINGS
WORD SENSE INDUCTION
GRAPHIC METHODS
SEMANTICS
EMBEDDINGS
LEXICAL SEMANTICS
SYNONYMS
SYNSET INDUCTION
WORD SENSE INDUCTIONS
IMAGE ANALYSIS
URI: http://hdl.handle.net/10995/102044
Access: info:eu-repo/semantics/openAccess
SCOPUS ID: 85039432105
PURE ID: 6253170
4bfa9646-fdbe-4a3f-9d83-f4a030afbbcc
ISSN: 3029743
ISBN: 9783319730127
DOI: 10.1007/978-3-319-73013-4_9
Appears in Collections:Научные публикации, проиндексированные в SCOPUS и WoS CC

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