Please use this identifier to cite or link to this item:
https://elar.urfu.ru/handle/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://elar.urfu.ru/handle/10995/102149 |
Access: | info:eu-repo/semantics/openAccess |
SCOPUS ID: | 85014186549 |
WOS ID: | 000407059600021 |
PURE ID: | f53106f2-e7a5-4a5d-a5f0-c59c09fb43ef 1611440 |
ISSN: | 18650929 |
ISBN: | 9783319529196 |
DOI: | 10.1007/978-3-319-52920-2_21 |
Appears in Collections: | Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC |
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