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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 Hits 21 – 36 of 36 21 Spatial multi-arrangement for clustering and multi-way similarity dataset construction Majewska, Olga; McCarthy, D; van den Bosch, J. - : European Language Resources Association, 2020. : LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings, 2020 BASE Show details 22 Manual Clustering and Spatial Arrangement of Verbs for Multilingual Evaluation and Typology Analysis Majewska, Olga; Vulic, Ivan; McCarthy, Diana. - : International Committee on Computational Linguistics, 2020. : https://www.aclweb.org/anthology/2020.coling-main.423, 2020. : Proceedings of the 28th International Conference on Computational Linguistics (COLING 2020), 2020 BASE Show details 23 XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning Liu, Qianchu; Korhonen, Anna-Leena; Majewska, Olga. - : Association for Computational Linguistics, 2020. : Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2020), 2020 BASE Show details 24 Common sense or world knowledge? Investigating adapter-based knowledge injection into pretrained transformers Lauscher, Anne; Majewska, Olga; Ribeiro, Leonardo F. R.. - : Association for Computational Linguistics, 2020 BASE Show details 25 XCOPA: A multilingual dataset for causal commonsense reasoning Ponti, Edoardo Maria; Majewska, Olga; Liu, Qianchu. - : Association for Computational Linguistics, 2020 BASE Show details 26 A neural classification method for supporting the creation of BioVerbNet ... Chiu, Billy; Majewska, Olga; Pyysalo, Sampo. - : Figshare, 2019 BASE Show details 27 A neural classification method for supporting the creation of BioVerbNet ... Chiu, Billy; Majewska, Olga; Pyysalo, Sampo. - : Figshare, 2019 BASE Show details 28 A neural classification method for supporting the creation of BioVerbNet ... Chiu, Billy; Majewska, Olga; Pyysalo, Sampo. - : Apollo - University of Cambridge Repository, 2019 BASE Show details 29 A Neural Classification Method for Supporting the Creation of BioVerbNet ... Chiu, Hon Wing; Majewska, Olga; Pyysalo, Sampo. - : Apollo - University of Cambridge Repository, 2019 BASE Show details 30 A Neural Classification Method for Supporting the Creation of BioVerbNet Chiu, Hon Wing; Majewska, Olga; Pyysalo, Sampo. - : BioMed Central, 2019. : https://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-018-0193-x, 2019. : Journal of Biomedical Semantics, 2019 BASE Show details 31 A neural classification method for supporting the creation of BioVerbNet Chiu, Billy; Majewska, Olga; Pyysalo, Sampo; Wey, Laura; Stenius, Ulla; Korhonen, Anna; Palmer, Martha. - : BioMed Central, 2019 Abstract: Abstract Background VerbNet, an extensive computational verb lexicon for English, has proved useful for supporting a wide range of Natural Language Processing tasks requiring information about the behaviour and meaning of verbs. Biomedical text processing and mining could benefit from a similar resource. We take the first step towards the development of BioVerbNet: A VerbNet specifically aimed at describing verbs in the area of biomedicine. Because VerbNet-style classification is extremely time consuming, we start from a small manual classification of biomedical verbs and apply a state-of-the-art neural representation model, specifically developed for class-based optimization, to expand the classification with new verbs, using all the PubMed abstracts and the full articles in the PubMed Central Open Access subset as data. Results Direct evaluation of the resulting classification against BioSimVerb (verb similarity judgement data in biomedicine) shows promising results when representation learning is performed using verb class-based contexts. Human validation by linguists and biologists reveals that the automatically expanded classification is highly accurate. Including novel, valid member verbs and classes, our method can be used to facilitate cost-effective development of BioVerbNet. Conclusion This work constitutes the first effort on applying a state-of-the-art architecture for neural representation learning to biomedical verb classification. While we discuss future optimization of the method, our promising results suggest that the automatic classification released with this article can be used to readily support application tasks in biomedicine. URL: https://doi.org/10.17863/CAM.35554 https://www.repository.cam.ac.uk/handle/1810/288240 BASE Hide details 32 Acquiring verb classes through bottom-up semantic verb clustering ... Majewska, Olga; McCarthy, D; Vulić, I. - : Apollo - University of Cambridge Repository, 2018 BASE Show details 33 Investigating the cross-lingual translatability of VerbNet-style classification. ... Majewska, Olga; Vulić, Ivan; McCarthy, Diana. - : Apollo - University of Cambridge Repository, 2018 BASE Show details 34 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 35 Acquiring verb classes through bottom-up semantic verb clustering Majewska, Olga; McCarthy, D; Vulić, I. - : LREC 2018 - 11th International Conference on Language Resources and Evaluation, 2018 BASE Show details 36 Investigating the cross-lingual translatability of VerbNet-style classification [<Journal>] Majewska, Olga [Verfasser]; Vulić, Ivan [Sonstige]; McCarthy, Diana [Sonstige]. DNB Subject Category Language Show details Page: 1 2

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