Human and Machine Judgements for Russian Semantic Relatedness

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

doi:10.1007/978-3-319-52920-2_21

arXiv · 1708.09702

Human and Machine Judgements for Russian Semantic Relatedness

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

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Abstract

Semantic relatedness of terms represents similarity of meaning by a numerical score. On the one hand, humans easily make judgments 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 judgments, 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 (word_i, word_j, relatedness_ij). 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 organize 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.

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Alexander Panchenko, Dmitry Ustalov, Nikolay Arefyev, Denis Paperno, Natalia Konstantinova, Natalia Loukachevitch, Chris Biemann. 2017-08-31. Human and Machine Judgements for Russian Semantic Relatedness. https://doi.org/10.1007/978-3-319-52920-2_21

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