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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 – 14 of 14 1 Lightweight Cross-Lingual Sentence Representation Learning ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Chu, Chenhui; Gupta, Prakhar. - : Underline Science Inc., 2021 BASE Show details 2 Obtaining Better Static Word Embeddings Using Contextual Embedding Models ... Gupta, Prakhar; Jaggi, Martin. - : arXiv, 2021 BASE Show details 3 Obtaining Better Static Word Embeddings Using Contextual Embedding Models ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Gupta, Prakhar; Jaggi, Martin. - : Underline Science Inc., 2021 BASE Show details 4 Correlating Twitter Language with Community-Level Health Outcomes Jaggi, Martin; Grubenmann, Ralf; Logean, Severine Rion... In: http://infoscience.epfl.ch/record/278185 (2020) BASE Show details 5 Robust Cross-lingual Embeddings from Parallel Sentences ... Sabet, Ali; Gupta, Prakhar; Cordonnier, Jean-Baptiste. - : arXiv, 2019 BASE Show details 6 Crosslingual Document Embedding as Reduced-Rank Ridge Regression ... Josifoski, Martin; Paskov, Ivan S.; Paskov, Hristo S.; Jaggi, Martin; West, Robert. - : arXiv, 2019 Abstract: There has recently been much interest in extending vector-based word representations to multiple languages, such that words can be compared across languages. In this paper, we shift the focus from words to documents and introduce a method for embedding documents written in any language into a single, language-independent vector space. For training, our approach leverages a multilingual corpus where the same concept is covered in multiple languages (but not necessarily via exact translations), such as Wikipedia. Our method, Cr5 (Crosslingual reduced-rank ridge regression), starts by training a ridge-regression-based classifier that uses language-specific bag-of-word features in order to predict the concept that a given document is about. We show that, when constraining the learned weight matrix to be of low rank, it can be factored to obtain the desired mappings from language-specific bags-of-words to language-independent embeddings. As opposed to most prior methods, which use pretrained monolingual word ... : In The Twelfth ACM International Conference on Web Search and Data Mining (WSDM '19) ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.1904.03922 https://arxiv.org/abs/1904.03922 BASE Hide details 7 System-Aware Algorithms For Machine Learning Mendler-Dünner, Celestine. - : ETH Zurich, 2019 BASE Show details 8 Crosslingual Document Embedding as Reduced-Rank Ridge Regression Paskov, Ivan S.; West, Robert; Paskov, Hristo S.... In: http://infoscience.epfl.ch/record/263893 (2019) BASE Show details 9 Leveraging Large Amounts of Weakly Supervised Data for Multi-Language Sentiment Classification ... Deriu, Jan; Lucchi, Aurelien; De Luca, Valeria. - : arXiv, 2017 BASE Show details 10 Generating Steganographic Text with LSTMs ... Fang, Tina; Jaggi, Martin; Argyraki, Katerina. - : arXiv, 2017 BASE Show details 11 Leveraging large amounts of weakly supervised data for multi-language sentiment classification ... Deriu, Jan Milan; Lucchi, Aurelien; De Luca, Valeria. - : ACM, 2017 BASE Show details 12 Leveraging large amounts of weakly supervised data for multi-language sentiment classification ... Deriu, Jan; Lucchi, Aurélien; De Luca, Valeria. - : ETH Zurich, 2017 BASE Show details 13 Generating Steganographic Text with LSTMs Tina Fang, Tina; Jaggi, Martin; Argyraki, Katerina In: http://infoscience.epfl.ch/record/229881 (2017) BASE Show details 14 Swiss-chocolate : sentiment detection using sparse SVMs and part-of-speech n-grams ... Jaggi, Martin; Uzdilli, Fatih; Cieliebak, Mark. - : Association for Computational Linguistics, 2014 BASE Show details

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