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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 4 5 6 7 8 9 10 Hits 81 – 100 of 191 81 XCOPA: A multilingual dataset for causal commonsense reasoning Ponti, Edoardo Maria; Majewska, Olga; Liu, Qianchu. - : Association for Computational Linguistics, 2020 BASE Show details 82 Improving bilingual lexicon induction with unsupervised post-processing of monolingual word vector spaces Glavaš, Goran; Korhonen, Anna; Vulić, Ivan. - : Association for Computational Linguistics, 2020 BASE Show details 83 SemEval-2020 Task 2: Predicting multilingual and cross-lingual (graded) lexical entailment Glavaš, Goran; Vulić, Ivan; Korhonen, Anna. - : Association for Computational Linguistics, 2020 BASE Show details 84 Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing Ponti, Edoardo Maria; O’Horan, Helen; Berzak, Yevgeni... In: ISSN: 0891-2017 ; EISSN: 1530-9312 ; Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02425462 ; Computational Linguistics, Massachusetts Institute of Technology Press (MIT Press), 2019, 45 (3), pp.559-601. ⟨10.1162/coli_a_00357⟩ ; https://www.mitpressjournals.org/doi/abs/10.1162/coli_a_00357 (2019) BASE Show details 85 Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing ... Ponti, Edoardo; O'Horan, Helen; Berzak, Yevgeni. - : Apollo - University of Cambridge Repository, 2019 BASE Show details 86 Show Some Love to Your n-grams: A Bit of Progress and Stronger n-gram Language Modeling Baselines ... Shareghi, Ehsan; Gerz, Daniela; Vulic, Ivan. - : Apollo - University of Cambridge Repository, 2019 BASE Show details 87 Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ... Lauscher, Anne; Vulić, Ivan; Ponti, Edoardo Maria. - : arXiv, 2019 BASE Show details 88 Do We Really Need Fully Unsupervised Cross-Lingual Embeddings? ... Vulić, Ivan; Glavaš, Goran; Reichart, Roi. - : arXiv, 2019 BASE Show details 89 A neural classification method for supporting the creation of BioVerbNet ... Chiu, Billy; Majewska, Olga; Pyysalo, Sampo. - : Figshare, 2019 BASE Show details 90 A neural classification method for supporting the creation of BioVerbNet ... Chiu, Billy; Majewska, Olga; Pyysalo, Sampo. - : Figshare, 2019 BASE Show details 91 Investigating cross-lingual alignment methods for contextualized embeddings with Token-level evaluation ... Liu, Qianchu; McCarthy, D; Vulić, I. - : Apollo - University of Cambridge Repository, 2019 BASE Show details 92 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 93 Second-order contexts from lexical substitutes for few-shot learning of word representations ... Liu, Qianchu; McCarthy, D; Korhonen, Anna-Leena. - : Apollo - University of Cambridge Repository, 2019 BASE Show details 94 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 95 Enhancing biomedical word embeddings by retrofitting to verb clusters ... Chiu, B; Baker, Simon; Palmer, M. - : Apollo - University of Cambridge Repository, 2019 BASE Show details 96 Crowdsourcing and Aggregating Nested Markable Annotations Madge, Chris; Yu, Juntao; Chamberlain, Jon. - : Association for Computational Linguistics, 2019 BASE Show details 97 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 98 Second-order contexts from lexical substitutes for few-shot learning of word representations Liu, Qianchu; McCarthy, D; Korhonen, Anna-Leena. - : *SEM@NAACL-HLT 2019 - 8th Joint Conference on Lexical and Computational Semantics, 2019 BASE Show details 99 Investigating cross-lingual alignment methods for contextualized embeddings with Token-level evaluation Liu, Qianchu; McCarthy, D; Vulić, I. - : CoNLL 2019 - 23rd Conference on Computational Natural Language Learning, Proceedings of the Conference, 2019 BASE Show details 100 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 Page: 1 2 3 4 5 6 7 8 9 10

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