Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/102149
Title: Human and machine judgements for Russian semantic relatedness
Authors: Panchenko, A.
Ustalov, D.
Arefyev, N.
Paperno, D.
Konstantinova, N.
Loukachevitch, N.
Biemann, C.
Issue Date: 2017
Publisher: Springer Verlag
Citation: Human and machine judgements for Russian semantic relatedness / A. Panchenko, D. Ustalov, N. Arefyev, et al. — DOI 10.1007/978-3-319-52920-2_21 // Communications in Computer and Information Science. — 2017. — Vol. 661. — P. 221-235.
Abstract: Semantic relatedness of terms represents similarity of meaning by a numerical score. On the one hand, humans easily make judgements about semantic relatedness. On the other hand, this kind of information is useful in language processing systems. While semantic relatedness has been extensively studied for English using numerous language resources, such as associative norms, human judgements and datasets generated from lexical databases, no evaluation resources of this kind have been available for Russian to date. Our contribution addresses this problem. We present five language resources of different scale and purpose for Russian semantic relatedness, each being a list of triples (wordi, wordj, similarityij). Four of them are designed for evaluation of systems for computing semantic relatedness, complementing each other in terms of the semantic relation type they represent. These benchmarks were used to organise a shared task on Russian semantic relatedness, which attracted 19 teams. We use one of the best approaches identified in this competition to generate the fifth high-coverage resource, the first open distributional thesaurus of Russian. Multiple evaluations of this thesaurus, including a large-scale crowdsourcing study involving native speakers, indicate its high accuracy. © Springer International Publishing AG 2017.
Keywords: CROWDSOURCING
DISTRIBUTIONAL THESAURUS
EVALUATION
LANGUAGE RESOURCES
SEMANTIC RELATEDNESS
SEMANTIC SIMILARITY
CROWDSOURCING
IMAGE ANALYSIS
NATURAL LANGUAGE PROCESSING SYSTEMS
THESAURI
EVALUATION
HIGH-ACCURACY
LANGUAGE PROCESSING SYSTEMS
LANGUAGE RESOURCES
LEXICAL DATABASE
SEMANTIC RELATEDNESS
SEMANTIC RELATIONS
SEMANTIC SIMILARITY
SEMANTICS
URI: http://hdl.handle.net/10995/102149
Access: info:eu-repo/semantics/openAccess
SCOPUS ID: 85014186549
PURE ID: 1611440
f53106f2-e7a5-4a5d-a5f0-c59c09fb43ef
ISSN: 18650929
ISBN: 9783319529196
DOI: 10.1007/978-3-319-52920-2_21
Appears in Collections:Научные публикации, проиндексированные в SCOPUS и WoS CC

Files in This Item:
File Description SizeFormat 
2-s2.0-85014186549.pdf821,41 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.