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http://elar.urfu.ru/handle/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://elar.urfu.ru/handle/10995/102044 |
Access: | info:eu-repo/semantics/openAccess |
RSCI ID: | 35500784 |
SCOPUS ID: | 85039432105 |
WOS ID: | 000441461800009 |
PURE ID: | 4bfa9646-fdbe-4a3f-9d83-f4a030afbbcc 6253170 |
ISSN: | 3029743 |
ISBN: | 9783319730127 |
DOI: | 10.1007/978-3-319-73013-4_9 |
Appears in Collections: | Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC |
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