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    • Glavaš, Goran (12)
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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 – 12 of 12 1 Unsupervised Cross-Lingual Information Retrieval using Monolingual Data Only ... Litschko, Robert; Glavaš, Goran; Ponzetto, Simone Paolo. - : arXiv, 2018 BASE Show details 2 Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization ... Ponti, Edoardo Maria; Vulić, Ivan; Glavaš, Goran. - : arXiv, 2018 BASE Show details 3 Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ... Vulić, Ivan; Glavaš, Goran; Mrkšić, Nikola. - : arXiv, 2018 BASE Show details 4 A Resource-Light Method for Cross-Lingual Semantic Textual Similarity ... Glavaš, Goran; Franco-Salvador, Marc; Ponzetto, Simone Paolo. - : arXiv, 2018 BASE Show details 5 Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources ... Vulic, Ivan; Glavaš, Goran; Mrkšić, Nikola; Korhonen, Anna-Leena. - : Apollo - University of Cambridge Repository, 2018 Abstract: Word vector specialisation (also known as retrofitting) is a portable, light-weight approach to fine-tuning arbitrary distributional word vector spaces by injecting external knowledge from rich lexical resources such as WordNet. By design, these post-processing methods only update the vectors of words occurring in external lexicons, leaving the representations of all unseen words intact. In this paper, we show that constraint-driven vector space specialisation can be extended to unseen words. We propose a novel post-specialisation method that: a) preserves the useful linguistic knowledge for seen words; while b) propagating this external signal to unseen words in order to improve their vector representations as well. Our post-specialisation approach explicits a non-linear specialisation function in the form of a deep neural network by learning to predict specialised vectors from their original distributional counterparts. The learned function is then used to specialise vectors of unseen words. This approach, ... : https://www.aclweb.org/anthology/N18-1048 ... URL: https://www.repository.cam.ac.uk/handle/1810/294076 https://dx.doi.org/10.17863/cam.41176 BASE Hide details 6 ArguminSci: a tool for analyzing argumentation and rhetorical aspects in scientific writing Glavaš, Goran; Lauscher, Anne; Eckert, Kai. - : Association for Computational Linguistics, 2018 BASE Show details 7 An argument-annotated corpus of scientific publications Ponzetto, Simone Paolo; Lauscher, Anne; Glavaš, Goran. - : Association for Computational Linguistics, 2018 BASE Show details 8 Investigating the role of argumentation in the rhetorical analysis of scientific publications with neural multi-task learning models Ponzetto, Simone Paolo; Eckert, Kai; Lauscher, Anne. - : Association for Computational Linguistics, 2018 BASE Show details 9 Post-specialisation: Retrofitting vectors of words unseen in lexical resources Mrkšić, Nikola; Glavaš, Goran; Korhonen, Anna. - : Association for Computational Linguistics, 2018 BASE Show details 10 Discriminating between lexico-semantic relations with the specialization tensor model Vulić, Ivan; Glavaš, Goran. - : Association for Computational Linguistics, 2018 BASE Show details 11 Adversarial propagation and zero-shot cross-lingual transfer of word vector specialization Ponti, Edoardo Maria; Vulić, Ivan; Glavaš, Goran. - : Association for Computational Linguistics, 2018 BASE Show details 12 Explicit retrofitting of distributional word vectors Glavaš, Goran; Vulić, Ivan. - : Association for Computational Linguistics, 2018 BASE Show details

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