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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 4 5 6 Hits 81 – 100 of 112 81 Post-specialisation: Retrofitting vectors of words unseen in lexical resources Mrkšić, Nikola; Glavaš, Goran; Korhonen, Anna. - : Association for Computational Linguistics, 2018 BASE Show details 82 Discriminating between lexico-semantic relations with the specialization tensor model Vulić, Ivan; Glavaš, Goran. - : Association for Computational Linguistics, 2018 BASE Show details 83 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 84 Explicit retrofitting of distributional word vectors Glavaš, Goran; Vulić, Ivan. - : Association for Computational Linguistics, 2018 BASE Show details 85 A resource-light method for cross-lingual semantic textual similarity Glavas, Goran; Rosso, Paolo; Franco-Salvador, Marc; Ponzetto, Simone Paolo. - : Elsevier, 2018 Abstract: [EN] Recognizing semantically similar sentences or paragraphs across languages is beneficial for many tasks, ranging from cross-lingual information retrieval and plagiarism detection to machine translation. Recently proposed methods for predicting cross-lingual semantic similarity of short texts, however, make use of tools and resources (e.g., machine translation systems, syntactic parsers or named entity recognition) that for many languages (or language pairs) do not exist. In contrast, we propose an unsupervised and a very resource-light approach for measuring semantic similarity between texts in different languages. To operate in the bilingual (or multilingual) space, we project continuous word vectors (i.e., word embeddings) from one language to the vector space of the other language via the linear translation model. We then align words according to the similarity of their vectors in the bilingual embedding space and investigate different unsupervised measures of semantic similarity exploiting bilingual embeddings and word alignments. Requiring only a limited-size set of word translation pairs between the languages, the proposed approach is applicable to virtually any pair of languages for which there exists a sufficiently large corpus, required to learn monolingual word embeddings. Experimental results on three different datasets for measuring semantic textual similarity show that our simple resource-light approach reaches performance close to that of supervised and resource-intensive methods, displaying stability across different language pairs. Furthermore, we evaluate the proposed method on two extrinsic tasks, namely extraction of parallel sentences from comparable corpora and cross-lingual plagiarism detection, and show that it yields performance comparable to those of complex resource-intensive state-of-the-art models for the respective tasks. (C) 2017 Published by Elsevier B.V. ; Part of the work presented in this article was performed during second author's research visit to the University of Mannheim, supported by Contact Fellowship awarded by the DAAD scholarship program "STIBET Doktoranden". The research of the last author has been carried out in the framework of the SomEMBED project (TIN2015-71147-C2-1-P). Furthermore, this work was partially funded by the Junior-professor funding programme of the Ministry of Science, Research and the Arts of the state of Baden-Wurttemberg (project "Deep semantic models for high-end NLP application"). ; Glavas, G.; Franco-Salvador, M.; Ponzetto, SP.; Rosso, P. (2018). A resource-light method for cross-lingual semantic textual similarity. Knowledge-Based Systems. 143:1-9. https://doi.org/10.1016/j.knosys.2017.11.041 ; S ; 1 ; 9 ; 143 Keyword: Cross-lingual Word embeddings; LENGUAJES Y SISTEMAS INFORMATICOS; Plagiarism detection; Semantic textual similarity; Word alignment Parallel sentences alignment URL: http://hdl.handle.net/10251/146277 https://doi.org/10.1016/j.knosys.2017.11.041 BASE Hide details 86 Cross-lingual classification of topics in political texts Glavaš, Goran [Verfasser]; Nanni, Federico [Verfasser]; Ponzetto, Simone Paolo [Verfasser]. - Mannheim : Universitätsbibliothek Mannheim, 2017 DNB Subject Category Language Show details 87 Improving neural knowledge base completion with cross-lingual projections Klein, Patrick [Verfasser]; Ponzetto, Simone Paolo [Verfasser]; Glavaš, Goran [Verfasser]. - Mannheim : Universitätsbibliothek Mannheim, 2017 DNB Subject Category Language Show details 88 Dual tensor model for detecting asymmetric lexico-semantic relations Glavaš, Goran [Verfasser]; Ponzetto, Simone Paolo [Verfasser]. - Mannheim : Universitätsbibliothek Mannheim, 2017 DNB Subject Category Language Show details 89 Unsupervised cross-lingual scaling of political texts Nanni, Federico; Ponzetto, Simone Paolo; Glavaš, Goran. - : Association for Computational Linguistics, 2017 BASE Show details 90 University of Mannheim @ CLSciSumm-17: Citation-Based Summarization of Scientific Articles Using Semantic Textual Similarity Eckert, Kai; Glavaš, Goran; Lauscher, Anne. - : RWTH, 2017 BASE Show details 91 Cross-lingual classification of topics in political texts Ponzetto, Simone Paolo; Nanni, Federico; Glavaš, Goran. - : Association for Computational Linguistics (ACL), 2017 BASE Show details 92 Improving neural knowledge base completion with cross-lingual projections Klein, Patrick; Glavaš, Goran; Ponzetto, Simone Paolo. - : Association for Computational Linguistics, 2017 BASE Show details 93 Leveraging event-based semantics for automated text simplification Štajner, Sanja; Glavaš, Goran. - : Elsevier, 2017 BASE Show details 94 Two layers of annotation for representing event mentions in news stories Buono, Maria Pia di; Tutek, Martin; Šnajder, Jan. - : Association for Computational Linguistics, 2017 BASE Show details 95 If sentences could see: Investigating visual information for semantic textual similarity Glavaš, Goran; Vulić, Ivan; Ponzetto, Simone Paolo. - : Association for Computational Linguistics, 2017 BASE Show details 96 Dual tensor model for detecting asymmetric lexico-semantic relations Glavaš, Goran; Ponzetto, Simone Paolo. - : Association for Computational Linguistics, 2017 BASE Show details 97 Predicting news values from headline text and emotions Buono, Maria Pia di; Šnajder, Jan; Dalbelo Bašić, Bojana. - : Association for Computational Linguistics, 2017 BASE Show details 98 Unsupervised text segmentation using semantic relatedness graphs Glavaš, Goran; Nanni, Federico; Ponzetto, Simone Paolo. - : Association for Computational Linguistics, 2016 BASE Show details 99 Spanish NER with word representations and conditional random fields Copara Zea, Jenny Linet; Ochoa Luna, José Eduardo; Thorne, Camilo. - : Association for Computational Linguistics, 2016 BASE Show details 100 Capturing interdisciplinarity in academic abstracts Nanni, Federico; Dietz, Laura; Faralli, Stefano. - : Corporation for National Research Initiatives, 2016 BASE Show details Page: 1 2 3 4 5 6

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