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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. - : arXiv, 2017 BASE Show 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 Abstract: Existing approaches to automatic VerbNet-style verb classification are heavily dependent on feature engineering and therefore limited to languages with mature NLP pipelines. In this work, we propose a novel cross-lingual transfer method for inducing VerbNets for multiple languages. To the best of our knowledge, this is the first study which demonstrates how the architectures for learning word embeddings can be applied to this challenging syntactic-semantic task. Our method uses cross-lingual translation pairs to tie each of the six target languages into a bilingual vector space with English, jointly specialising the representations to encode the relational information from English VerbNet. A standard clustering algorithm is then run on top of the VerbNet-specialised representations, using vector dimensions as features for learning verb classes. Our results show that the proposed cross-lingual transfer approach sets new state-of-the-art verb classification performance across all six target languages explored ... : EMNLP 2017 (long paper) ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/1707.06945 https://dx.doi.org/10.48550/arxiv.1707.06945 BASE Hide 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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