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arXiv 1803.05795cs.CL

RUSSE'2018: A Shared Task on Word Sense Induction for the Russian Language

University of Hamburg(汉堡大学);National Research University Higher School of Economics(国立研究型大学高等经济学院);The Vinogradov Institute of the Russian Language, Russian Academy of Sciences(俄罗斯科学院维诺格拉多夫俄语研究所);University of Mannheim(曼海姆大学);Krasovskii Institute of Mathematics and Mechanics(克拉索夫斯基数学与力学研究所);Scrapinghub(Scrapinghub公司);Lomonosov Moscow State University(罗蒙诺索夫莫斯科国立大学);Samsung Moscow Research Center(三星莫斯科研究中心);ABBYY(ABBYY公司)

Alexander Panchenko, Anastasiya Lopukhina, Dmitry Ustalov, Konstantin Lopukhin, Nikolay Arefyev, Alexey Leontyev, Natalia Loukachevitch

英文摘要:

The paper describes the results of the first shared task on word sense induction (WSI) for the Russian language. While similar shared tasks were conducted in the past for some Romance and Germanic languages, we explore the performance of sense induction and disambiguation methods for a Slavic language that shares many features with other Slavic languages, such as rich morphology and virtually free word order. The participants were asked to group contexts of a given word in accordance with its senses that were not provided beforehand. For instance, given a word "bank" and a set of contexts for this word, e.g. "bank is a financial institution that accepts deposits" and "river bank is a slope beside a body of water", a participant was asked to cluster such contexts in the unknown in advance number of clusters corresponding to, in this case, the "company" and the "area" senses of the word "bank". For the purpose of this evaluation campaign, we developed three new evaluation datasets based on sense inventories that have different sense granularity. The contexts in these datasets were sampled from texts of Wikipedia, the academic corpus of Russian, and an explanatory dictionary of Russian. Overall, 18 teams participated in the competition submitting 383 models. Multiple teams managed to substantially outperform competitive state-of-the-art baselines from the previous years based on sense embeddings.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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