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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 Hits 1 – 16 of 16 1 Bio-SimVerb ... Chiu, Hon Wing; Pyysalo, Sampo; Vulic, Ivan. - : Apollo - University of Cambridge Repository, 2018 BASE Show details 2 Isomorphic Transfer of Syntactic Structures in Cross-Lingual NLP ... Ponti, Edoardo; Reichart, Roi; Korhonen, Anna-Leena. - : Apollo - University of Cambridge Repository, 2018 BASE Show details 3 Acquiring verb classes through bottom-up semantic verb clustering ... Majewska, Olga; McCarthy, D; Vulić, I. - : Apollo - University of Cambridge Repository, 2018 BASE Show details 4 Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction ... Gerz, Daniela; Vulić, Ivan; Ponti, Edoardo. - : Apollo - University of Cambridge Repository, 2018 BASE Show details 5 Injecting Lexical Contrast into Word Vectors by Guiding Vector Space Specialisation ... Vulic, Ivan; Korhonen, Anna-Leena; Linguist, Assoc Computat. - : Apollo - University of Cambridge Repository, 2018 BASE Show details 6 Investigating the cross-lingual translatability of VerbNet-style classification. ... Majewska, Olga; Vulić, Ivan; McCarthy, Diana. - : Apollo - University of Cambridge Repository, 2018 BASE Show details 7 Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ... Vulic, Ivan; Glavaš, Goran; Mrkšić, Nikola. - : Apollo - University of Cambridge Repository, 2018 BASE Show details 8 Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction Gerz, Daniela; Vulić, Ivan; Ponti, Edoardo. - : MIT Press - Journals, 2018. : Transactions of the Association for Computational Linguistics, 2018 BASE Show details 9 Investigating the cross-lingual translatability of VerbNet-style classification. Majewska, Olga; Vulić, Ivan; McCarthy, Diana. - : Springer Science and Business Media LLC, 2018. : Lang Resour Eval, 2018 BASE Show details 10 Injecting Lexical Contrast into Word Vectors by Guiding Vector Space Specialisation Vulic, Ivan; Korhonen, Anna-Leena; Linguist, Assoc Computat. - : REPRESENTATION LEARNING FOR NLP, 2018 BASE Show details 11 Bio-SimVerb and Bio-SimLex: wide-coverage evaluation sets of word similarity in biomedicine. Chiu, Billy; Pyysalo, Sampo; Vulić, Ivan. - : BioMed Central, 2018. : BMC bioinformatics, 2018 BASE Show details 12 Bio-SimVerb and Bio-SimLex: wide-coverage evaluation sets of word similarity in biomedicine. Chiu, Billy; Pyysalo, Sampo; Vulić, Ivan. - : Springer Science and Business Media LLC, 2018. : BMC Bioinformatics, 2018 BASE Show details 13 Dependency parsing of learner English Huang, Yan; Murakami, Akira; Alexopoulou, Dora. - : John Benjamins Publishing Company, 2018. : International Journal of Corpus Linguistics, 2018 BASE Show details 14 Isomorphic Transfer of Syntactic Structures in Cross-Lingual NLP Vulic, Ivan; Ponti, Edoardo; Reichart, Roi. - : Association for Computational Linguistics, 2018. : Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018), 2018 BASE Show details 15 Bio-SimVerb Chiu, Hon Wing; Pyysalo, Sampo; Vulic, Ivan. - 2018 BASE Show details 16 Acquiring verb classes through bottom-up semantic verb clustering Majewska, Olga; McCarthy, D; Vulić, I; Korhonen, Anna-Leena. - : LREC 2018 - 11th International Conference on Language Resources and Evaluation, 2018 Abstract: In this paper, we present the first analysis of bottom-up manual semantic clustering of verbs in three languages, English, Polish and Croatian. Verb classes including syntactic and semantic information have been shown to support many NLP tasks by allowing abstraction from individual words and thereby alleviating data sparseness. The availability of such classifications is however still non-existent or limited in most languages. While a range of automatic verb classification approaches have been proposed, high-quality resources and gold standards are needed for evaluation and to improve the performance of NLP systems. We investigate whether semantic verb classes in three different languages can be reliably obtained from native speakers without linguistics training. The analysis of inter-annotator agreement shows an encouraging degree of overlap in the classifications produced for each language individually, as well as across all three languages. Comparative examination of the resultant classifications provides interesting insights into cross-linguistic semantic commonalities and patterns of ambiguity. URL: https://www.repository.cam.ac.uk/handle/1810/279155 https://doi.org/10.17863/CAM.26535 BASE Hide details

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