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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 Hits 1 – 20 of 26 1 On cross-lingual retrieval with multilingual text encoders Litschko, Robert; Vulić, Ivan; Ponzetto, Simone Paolo. - : Springer Science + Business Media, 2022 BASE Show details 2 On Cross-Lingual Retrieval with Multilingual Text Encoders ... Litschko, Robert; Vulić, Ivan; Ponzetto, Simone Paolo. - : arXiv, 2021 BASE Show details 3 Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval ... Litschko, Robert; Vulić, Ivan; Ponzetto, Simone Paolo. - : arXiv, 2021 BASE Show details 4 Evaluating multilingual text encoders for unsupervised cross-lingual retrieval Litschko, Robert; Vulić, Ivan; Ponzetto, Simone Paolo. - : Springer, 2021 BASE Show details 5 AraWEAT: Multidimensional Analysis of Biases in Arabic Word Embeddings ... Lauscher, Anne; Takieddin, Rafik; Ponzetto, Simone Paolo. - : arXiv, 2020 BASE Show details 6 SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment ... The 28th International Conference on Computational Linguistics 2020; Glavaš, Goran; Korhonen, Anna. - : Underline Science Inc., 2020 BASE Show details 7 SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment ... Glavas, Goran; Vulic, Ivan; Korhonen, Anna-Leena. - : Apollo - University of Cambridge Repository, 2020 BASE Show details 8 SemEval-2020 Task 2: Predicting Multilingual and Cross-Lingual (Graded) Lexical Entailment Glavas, Goran; Vulic, Ivan; Korhonen, Anna-Leena. - : International Committee for Computational Linguistics, 2020. : https://www.aclweb.org/anthology/2020.semeval-1.2, 2020. : Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval 2020), 2020 BASE Show details 9 AraWEAT: Multidimensional analysis of biases in Arabic word embeddings Lauscher, Anne; Takieddin, Rafik; Ponzetto, Simone Paolo. - : Association for Computational Linguistics, 2020 BASE Show details 10 SemEval-2020 Task 2: Predicting multilingual and cross-lingual (graded) lexical entailment Glavaš, Goran; Vulić, Ivan; Korhonen, Anna. - : Association for Computational Linguistics, 2020 BASE Show details 11 SEAGLE: A platform for comparative evaluation of semantic encoders for information retrieval Schmidt, Fabian David; Dietsche, Markus; Ponzetto, Simone Paolo. - : Association for Computational Linguistics, 2019 BASE Show details 12 Multilingual and cross-lingual graded lexical entailment Glavaš, Goran; Vulić, Ivan; Ponzetto, Simone Paolo. - : Association for Computational Linguistics, 2019 BASE Show details 13 Unsupervised Cross-Lingual Information Retrieval using Monolingual Data Only ... Litschko, Robert; Glavaš, Goran; Ponzetto, Simone Paolo. - : arXiv, 2018 BASE Show details 14 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 15 A Resource-Light Method for Cross-Lingual Semantic Textual Similarity ... Glavaš, Goran; Franco-Salvador, Marc; Ponzetto, Simone Paolo. - : arXiv, 2018 BASE Show details 16 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 17 An argument-annotated corpus of scientific publications Ponzetto, Simone Paolo; Lauscher, Anne; Glavaš, Goran. - : Association for Computational Linguistics, 2018 BASE Show details 18 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 19 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 20 Unsupervised cross-lingual scaling of political texts Nanni, Federico; Ponzetto, Simone Paolo; Glavaš, Goran. - : Association for Computational Linguistics, 2017 BASE Show details Page: 1 2

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