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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 – 9 of 9 1 RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Barikeri, Soumya; Glavaš, Goran. - : Underline Science Inc., 2021 BASE Show details 2 How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; ., Iryna; ., Sebastian. - : Underline Science Inc., 2021 BASE Show details 3 Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; ., Nigel; Korhonen, Anna. - : Underline Science Inc., 2021 BASE Show details 4 LexFit: Lexical Fine-Tuning of Pretrained Language Models ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Glavaš, Goran; Korhonen, Anna. - : Underline Science Inc., 2021 BASE Show details 5 A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; ., Hinrich; Korhonen, Anna. - : Underline Science Inc., 2021 BASE Show details 6 Multi-SimLex: A Large-Scale Evaluation of Multilingual and Cross-Lingual Lexical Semantic Similarity Vulic, Ivan; Baker, Simon; Ponti, Edoardo Maria... In: ISSN: 0891-2017 ; EISSN: 1530-9312 ; Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02975786 ; Computational Linguistics, Massachusetts Institute of Technology Press (MIT Press), 2020, 46 (4), pp.847-897 ; https://direct.mit.edu/coli/article/46/4/847/97326/Multi-SimLex-A-Large-Scale-Evaluation-of (2020) BASE Show details 7 A deep learning approach to bilingual lexicon induction in the biomedical domain. ... Heyman, Geert; Vulić, Ivan; Moens, Marie-Francine. - : Apollo - University of Cambridge Repository, 2018 BASE Show details 8 A deep learning approach to bilingual lexicon induction in the biomedical domain. Heyman, Geert; Vulić, Ivan; Moens, Marie-Francine. - : Springer Science and Business Media LLC, 2018. : BMC Bioinformatics, 2018 Abstract: BACKGROUND: Bilingual lexicon induction (BLI) is an important task in the biomedical domain as translation resources are usually available for general language usage, but are often lacking in domain-specific settings. In this article we consider BLI as a classification problem and train a neural network composed of a combination of recurrent long short-term memory and deep feed-forward networks in order to obtain word-level and character-level representations. RESULTS: The results show that the word-level and character-level representations each improve state-of-the-art results for BLI and biomedical translation mining. The best results are obtained by exploiting the synergy between these word-level and character-level representations in the classification model. We evaluate the models both quantitatively and qualitatively. CONCLUSIONS: Translation of domain-specific biomedical terminology benefits from the character-level representations compared to relying solely on word-level representations. It is beneficial to take a deep learning approach and learn character-level representations rather than relying on handcrafted representations that are typically used. Our combined model captures the semantics at the word level while also taking into account that specialized terminology often originates from a common root form (e.g., from Greek or Latin). Keyword: Data Mining; Deep Learning; Humans; Knowledge Bases; Multilingualism; Natural Language Processing; Semantics URL: https://www.repository.cam.ac.uk/handle/1810/288980 https://doi.org/10.17863/CAM.36243 BASE Hide details 9 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

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