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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 – 31 of 31 21 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 22 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 23 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 24 Bio-SimVerb and Bio-SimLex: wide-coverage evaluation sets of word similarity in biomedicine. Chiu, Billy; Pyysalo, Sampo; Vulić, Ivan; Korhonen, Anna-Leena. - : BioMed Central, 2018. : BMC bioinformatics, 2018 Abstract: Background: Word representations support a variety of Natural Language Processing (NLP) tasks. The quality of these representations is typically assessed by comparing the distances in the induced vector spaces against human similarity judgements. Whereas comprehensive evaluation resources have recently been developed for the general domain, similar resources for biomedicine currently suffer from the lack of coverage, both in terms of word types included and with respect to the semantic distinctions. Notably, verbs have been excluded, although they are essential for the interpretation of biomedical language. Further, current resources do not discern between semantic similarity and semantic relatedness, although this has been proven as an important predictor of the usefulness of word representations and their performance in downstream applications. Results: We present two novel comprehensive resources targeting the evaluation of word representations in biomedicine. These resources, Bio-SimVerb and Bio-SimLex, address the previously mentioned problems, and can be used for evaluations of verb and noun representations respectively. In our experiments, we have computed the Pearson’s correlation between performances on intrinsic and extrinsic tasks using twelve popular state-of-the-art representation models (e.g. word2vec models). The intrinsic–extrinsic correlations using our datasets are notably higher than with previous intrinsic evaluation benchmarks such as UMNSRS and MayoSRS. In addition, when evaluating representation models for their abilities to capture verb and noun semantics individually, we show a considerable variation between performances across all models. Conclusion: Bio-SimVerb and Bio-SimLex enable intrinsic evaluation of word representations. This evaluation can serve as a predictor of performance on various downstream tasks in the biomedical domain. The results on Bio-SimVerb and Bio-SimLex using standard word representation models highlight the importance of developing dedicated evaluation resources for NLP in biomedicine for particular word classes (e.g. verbs). These are needed to identify the most accurate methods for learning class-specific representations. Bio-SimVerb and Bio-SimLex are publicly available. Keyword: Biomedical Technology; Databases as Topic; Humans; Language; Natural Language Processing; Semantics; Software URL: https://doi.org/10.17863/CAM.18170 https://www.repository.cam.ac.uk/handle/1810/276650 BASE Hide details 25 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 26 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 27 Bio-SimVerb Chiu, Hon Wing; Pyysalo, Sampo; Vulic, Ivan. - 2018 BASE Show details 28 Automatic Selection of Context Configurations for Improved Class-Specific Word Representations ... Vulić, Ivan; Schwartz, Roy; Rappoport, Ari. - : Apollo - University of Cambridge Repository, 2017 BASE Show details 29 Morph-fitting: Fine-tuning word vector spaces with simple language-specific rules ... Vulic, Ivan; Mrkšic, N; Reichart, R. - : Apollo - University of Cambridge Repository, 2017 BASE Show details 30 Morph-fitting: Fine-tuning word vector spaces with simple language-specific rules Vulic, Ivan; Mrkšic, N; Reichart, R. - : Association for Computational Linguistics, 2017. : ACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers), 2017 BASE Show details 31 Automatic Selection of Context Configurations for Improved Class-Specific Word Representations Rappoport, Ari; Reichart, Roi; Korhonen, Anna-Leena. - : Association for Computational Linguistics, 2017. : https://arxiv.org/pdf/1608.05528.pdf, 2017. : Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017), 2017 BASE Show details Page: 1 2

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