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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 Normalization of historical texts with neural network models Bollmann, Marcel [Verfasser]; Dipper, Stefanie [Gutachter]; Plank, Barbara [Gutachter]. - Bochum : Ruhr-Universität Bochum, 2018 DNB Subject Category Language Show details 2 Universal Dependencies 2.2 Nivre, Joakim; Abrams, Mitchell; Agić, Željko... In: https://hal.archives-ouvertes.fr/hal-01930733 ; 2018 (2018) BASE Show details 3 Universal Dependencies 2.3 Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2018 BASE Show details 4 Universal Dependencies 2.2 Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2018 BASE Show details 5 Beyond task success: A closer look at jointly learning to see, ask, and GuessWhat ... Shekhar, Ravi; Venkatesh, Aashish; Baumgärtner, Tim. - : arXiv, 2018 BASE Show details 6 Distant Supervision from Disparate Sources for Low-Resource Part-of-Speech Tagging ... Plank, Barbara; Agić, Željko. - : arXiv, 2018 BASE Show details 7 The Best of Both Worlds: Lexical Resources To Improve Low-Resource Part-of-Speech Tagging ... Plank, Barbara; Klerke, Sigrid; Agic, Zeljko. - : arXiv, 2018 Abstract: In natural language processing, the deep learning revolution has shifted the focus from conventional hand-crafted symbolic representations to dense inputs, which are adequate representations learned automatically from corpora. However, particularly when working with low-resource languages, small amounts of symbolic lexical resources such as user-generated lexicons are often available even when gold-standard corpora are not. Such additional linguistic information is though often neglected, and recent neural approaches to cross-lingual tagging typically rely only on word and subword embeddings. While these representations are effective, our recent work has shown clear benefits of combining the best of both worlds: integrating conventional lexical information improves neural cross-lingual part-of-speech (PoS) tagging. However, little is known on how complementary such additional information is, and to what extent improvements depend on the coverage and quality of these external resources. This paper seeks to ... : Under review for Natural Language Engineering ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.1811.08757 https://arxiv.org/abs/1811.08757 BASE Hide details 8 Bleaching Text: Abstract Features for Cross-lingual Gender Prediction ... van der Goot, Rob; Ljubešić, Nikola; Matroos, Ian. - : arXiv, 2018 BASE Show details

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