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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 – 8 of 8 1 Evaluating multilingual text encoders for unsupervised cross-lingual retrieval Litschko, Robert; Vulić, Ivan; Ponzetto, Simone Paolo. - : Springer, 2021 BASE Show details 2 A deep learning approach to bilingual lexicon induction in the biomedical domain ... Heyman, Geert; Vulić, Ivan; Moens, Marie-Francine. - : Apollo - University of Cambridge Repository, 2018 BASE Show details 3 A deep learning approach to bilingual lexicon induction in the biomedical domain. ... Heyman, Geert; Vulić, Ivan; Moens, Marie-Francine. - : Apollo - University of Cambridge Repository, 2018 BASE Show details 4 A deep learning approach to bilingual lexicon induction in the biomedical domain. Heyman, Geert; Vulić, Ivan; Moens, Marie-Francine. - : Springer Science and Business Media LLC, 2018. : BMC Bioinformatics, 2018 BASE Show details 5 A deep learning approach to bilingual lexicon induction in the biomedical domain Heyman, Geert; Vulić, Ivan; Moens, Marie-Francine. - 2018 BASE Show details 6 A deep learning approach to bilingual lexicon induction in the biomedical domain Heyman, Geert; Vulić, Ivan; Moens, Marie-Francine. - : BioMed Central, 2018 BASE Show details 7 Bilingual Distributed Word Representations from Document-Aligned Comparable Data ... Vulić, Ivan; Moens, Marie-Francine. - : arXiv, 2015 Abstract: We propose a new model for learning bilingual word representations from non-parallel document-aligned data. Following the recent advances in word representation learning, our model learns dense real-valued word vectors, that is, bilingual word embeddings (BWEs). Unlike prior work on inducing BWEs which heavily relied on parallel sentence-aligned corpora and/or readily available translation resources such as dictionaries, the article reveals that BWEs may be learned solely on the basis of document-aligned comparable data without any additional lexical resources nor syntactic information. We present a comparison of our approach with previous state-of-the-art models for learning bilingual word representations from comparable data that rely on the framework of multilingual probabilistic topic modeling (MuPTM), as well as with distributional local context-counting models. We demonstrate the utility of the induced BWEs in two semantic tasks: (1) bilingual lexicon extraction, (2) suggesting word translations in ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/1509.07308 https://dx.doi.org/10.48550/arxiv.1509.07308 BASE Hide details 8 TKLBLIIR: Detecting Twitter paraphrases with TweetingJay Karan, Mladen; Glavaš, Goran; Šnajder, Jan. - : Association for Computational Linguistics, 2015 BASE Show details

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