Human and machine judgements for Russian semantic relatedness

Panchenko, Alexander ; Ustalov, Dmitry ; Arefyev, Nikolay ; Paperno, Denis ; Konstantinova, Natalia ; Loukachevitch, Natalia ; Biemann, Chris

DOI:	https://doi.org/10.1007/978-3-319-52920-2_21
URL:	https://arxiv.org/abs/1708.09702
Weitere URL:	https://www.lt.informatik.tu-darmstadt.de/fileadmi...
Dokumenttyp:	Konferenzveröffentlichung
Erscheinungsjahr:	2017
Buchtitel:	Analysis of Images, Social Networks and Texts : 5th International Conference, AIST 2016, Yekaterinburg, Russia, April 7-9, 2016, Revised Selected Papers
Titel einer Zeitschrift oder einer Reihe:	Communications in Computer and Information Science
Band/Volume:	661
Seitenbereich:	221-235
Veranstaltungstitel:	5th International Conference on Analysis of Images, Social Networks and Texts, AIST 2016
Veranstaltungsort:	Yekaterinburg, Russia
Veranstaltungsdatum:	April 7-9, 2016
Herausgeber:	Ignatov, Dmitry I.
Ort der Veröffentlichung:	Cham
Verlag:	Springer
ISBN:	978-3-319-52919-6 , 978-3-319-52920-2
ISSN:	1865-0929 , 1865-0937
Verwandte URLs:	https://de.slideshare.net/aistconf/alexa...
Sprache der Veröffentlichung:	Englisch
Einrichtung:	Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Information Systems III: Enterprise Data Analysis (Ponzetto 2016-)
Fachgebiet:	004 Informatik
Freie Schlagwörter (Englisch):	Semantic similarity , Semantic relatedness , Evaluation , Distributional thesaurus , Crowdsourcing , Language resources
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.