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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 Hits 41 – 50 of 50 41 A Survey Of Cross-lingual Word Embedding Models ... Ruder, Sebastian; Vulić, Ivan; Søgaard, Anders. - : arXiv, 2017 BASE Show details 42 Specialising Word Vectors for Lexical Entailment ... Vulić, Ivan; Mrkšić, Nikola. - : arXiv, 2017 BASE Show details 43 Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints ... Mrkšić, Nikola; Vulić, Ivan; Séaghdha, Diarmuid Ó. - : arXiv, 2017 BASE Show details 44 Morph-fitting: Fine-Tuning Word Vector Spaces with Simple Language-Specific Rules ... Vulić, Ivan; Mrkšić, Nikola; Reichart, Roi; Séaghdha, Diarmuid Ó; Young, Steve; Korhonen, Anna. - : arXiv, 2017 Abstract: Morphologically rich languages accentuate two properties of distributional vector space models: 1) the difficulty of inducing accurate representations for low-frequency word forms; and 2) insensitivity to distinct lexical relations that have similar distributional signatures. These effects are detrimental for language understanding systems, which may infer that 'inexpensive' is a rephrasing for 'expensive' or may not associate 'acquire' with 'acquires'. In this work, we propose a novel morph-fitting procedure which moves past the use of curated semantic lexicons for improving distributional vector spaces. Instead, our method injects morphological constraints generated using simple language-specific rules, pulling inflectional forms of the same word close together and pushing derivational antonyms far apart. In intrinsic evaluation over four languages, we show that our approach: 1) improves low-frequency word estimates; and 2) boosts the semantic quality of the entire word vector collection. Finally, we show ... : ACL 2017 (Long paper) ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.1706.00377 https://arxiv.org/abs/1706.00377 BASE Hide details 45 Decoding Sentiment from Distributed Representations of Sentences ... Ponti, Edoardo Maria; Vulić, Ivan; Korhonen, Anna. - : arXiv, 2017 BASE Show details 46 Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation ... Vulić, Ivan; Mrkšić, Nikola; Korhonen, Anna. - : arXiv, 2017 BASE Show details 47 Survey on the Use of Typological Information in Natural Language Processing ... O'Horan, Helen; Berzak, Yevgeni; Vulić, Ivan. - : arXiv, 2016 BASE Show details 48 Automatic Selection of Context Configurations for Improved Class-Specific Word Representations ... Vulić, Ivan; Schwartz, Roy; Rappoport, Ari. - : arXiv, 2016 BASE Show details 49 HyperLex: A Large-Scale Evaluation of Graded Lexical Entailment ... Vulić, Ivan; Gerz, Daniela; Kiela, Douwe. - : arXiv, 2016 BASE Show details 50 Bilingual Distributed Word Representations from Document-Aligned Comparable Data ... Vulić, Ivan; Moens, Marie-Francine. - : arXiv, 2015 BASE Show details Page: 1 2 3

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