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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 – 11 of 11 1 Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing Ponti, Edoardo,; O 'horan, Helen; Berzak, Yevgeni... In: https://hal.archives-ouvertes.fr/hal-01856176 ; 2018 (2018) BASE Show details 2 Unsupervised Cross-Lingual Information Retrieval Using Monolingual Data Only ... Litschko, Robert; Glavas, Goran; Ponzetto, Simone Paolo. - : Apollo - University of Cambridge Repository, 2018 BASE Show details 3 Bio-SimVerb ... Chiu, Hon Wing; Pyysalo, Sampo; Vulic, Ivan. - : Apollo - University of Cambridge Repository, 2018 BASE Show details 4 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 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 Specialising Word Vectors for Lexical Entailment ... Vulic, Ivan; Mrk�I?, Nikola. - : Apollo - University of Cambridge Repository, 2018 Abstract: We present LEAR (Lexical Entailment Attract-Repel), a novel post-processing method that transforms any input word vector space to emphasise the asymmetric relation of lexical entailment (LE), also known as the IS-A or hyponymy-hypernymy relation. By injecting external linguistic constraints (e.g., WordNet links) into the initial vector space, the LE specialisation procedure brings true hyponymy-hypernymy pairs closer together in the transformed Euclidean space. The proposed asymmetric distance measure adjusts the norms of word vectors to reflect the actual WordNet-style hierarchy of concepts. Simultaneously, a joint objective enforces semantic similarity using the symmetric cosine distance, yielding a vector space specialised for both lexical relations at once. LEAR specialisation achieves state-of-the-art performance in the tasks of hypernymy directionality, hypernymy detection, and graded lexical entailment, demonstrating the effectiveness and robustness of the proposed asymmetric specialisation model. ... URL: https://dx.doi.org/10.17863/cam.41175 https://www.repository.cam.ac.uk/handle/1810/294075 BASE Hide 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 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 9 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 10 Bio-SimVerb Chiu, Hon Wing; Pyysalo, Sampo; Vulic, Ivan. - 2018 BASE Show details 11 Unsupervised Cross-Lingual Information Retrieval Using Monolingual Data Only Litschko, Robert; Glavas, Goran; Ponzetto, Simone Paolo. - : ACM, 2018. : ACM/SIGIR PROCEEDINGS 2018, 2018 BASE Show details

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