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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 Hits 41 – 49 of 49 41 Using linked disambiguated distributional networks for word sense disambiguation Panchenko, Alexander; Faralli, Stefano; Ponzetto, Simone Paolo. - : Association for Computational Linguistics, 2017 BASE Show details 42 Mnogoznal : an unsupervised system for word sense disambiguation Ustalov, Dmitry; Teslenko, Denis; Panchenko, Alexander. - : IEEE, 2017 BASE Show details 43 Watset : automatic induction of synsets from a graph of synonyms Ustalov, Dmitry; Panchenko, Alexander; Biemann, Chris. - : Association for Computational Linguistics, 2017 BASE Show details 44 A tool for effective extraction of synsets and semantic relations from BabelNet Ustalov, Dmitry; Panchenko, Alexander. - : IEEE, 2017 BASE Show details 45 Human and machine judgements for Russian semantic relatedness Panchenko, Alexander; Ustalov, Dmitry; Arefyev, Nikolay. - : Springer, 2017 BASE Show details 46 Human And Machine Judgements For Russian Semantic Relatedness ... Panchenko, Alexander; Ustalov, Dmitry; Arefyev, Nikolay. - : Zenodo, 2016 BASE Show details 47 Linked disambiguated distributional semantic networks Faralli, Stefano; Panchenko, Alexander; Biemann, Chris; Ponzetto, Simone Paolo. - : Springer, 2016 Abstract: We present a new hybrid lexical knowledge base that combines the contextual information of distributional models with the conciseness and precision of manually constructed lexical networks. The computation of our count-based distributional model includes the induction of word senses for single-word and multi-word terms, the disambiguation of word similarity lists, taxonomic relations extracted by patterns and context clues for disambiguation in context. In contrast to dense vector representations, our resource is human readable and interpretable, and thus can be easily embedded within the Semantic Web ecosystem. Keyword: 004 Informatik URL: https://madoc.bib.uni-mannheim.de/41302 https://doi.org/10.1007/978-3-319-46547-0_7 https://madoc.bib.uni-mannheim.de/41302/1/faralli16c.pdf https://madoc.bib.uni-mannheim.de/41302/ BASE Hide details 48 TAXI at SemEval-2016 Task 13: a taxonomy induction method based on lexico-syntactic patterns, substrings and focused crawling Fairon, Cédrick; Faralli, Stefano; Panchenko, Alexander. - : Association for Computational Linguistics, 2016 BASE Show details 49 RUSSE: the first workshop on Russian semantic similarity Ustalov, Dmitry; Meyer, Christian; Panchenko, Alexander. - : RSUH, 2015 BASE Show details Page: 1 2 3

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