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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 – 11 of 11 1 Universal Dependencies 2.9 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 2 Universal Dependencies 2.8.1 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 3 Universal Dependencies 2.8 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 4 Universal Dependencies 2.7 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020 BASE Show details 5 Universal Dependencies 2.6 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020 BASE Show details 6 The RELX Dataset and Matching the Multilingual Blanks for Cross-Lingual Relation Classification ... Köksal, Abdullatif; Özgür, Arzucan. - : arXiv, 2020 BASE Show details 7 A Hybrid Approach to Dependency Parsing: Combining Rules and Morphology with Deep Learning ... Özateş, Şaziye Betül; Özgür, Arzucan; Güngör, Tunga; Öztürk, Balkız. - : arXiv, 2020 Abstract: Fully data-driven, deep learning-based models are usually designed as language-independent and have been shown to be successful for many natural language processing tasks. However, when the studied language is low-resourced and the amount of training data is insufficient, these models can benefit from the integration of natural language grammar-based information. We propose two approaches to dependency parsing especially for languages with restricted amount of training data. Our first approach combines a state-of-the-art deep learning-based parser with a rule-based approach and the second one incorporates morphological information into the parser. In the rule-based approach, the parsing decisions made by the rules are encoded and concatenated with the vector representations of the input words as additional information to the deep network. The morphology-based approach proposes different methods to include the morphological structure of words into the parser network. Experiments are conducted on the IMST-UD ... : 25 pages, 7 figures ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; I.2.7; Machine Learning cs.LG URL: https://arxiv.org/abs/2002.10116 https://dx.doi.org/10.48550/arxiv.2002.10116 BASE Hide details 8 Resources for Turkish Dependency Parsing: Introducing the BOUN Treebank and the BoAT Annotation Tool ... Türk, Utku; Atmaca, Furkan; Özateş, Şaziye Betül. - : arXiv, 2020 BASE Show details 9 CoNLL 2018 Shared Task System Outputs Zeman, Daniel; Potthast, Martin; Duthoo, Elie. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2018 BASE Show details 10 Automated Neuroanatomical Relation Extraction: A Linguistically Motivated Approach with a PVT Connectivity Graph Case Study Gökdeniz, Erinç; Özgür, Arzucan; Canbeyli, Reşit. - : Frontiers Media S.A., 2016 BASE Show details 11 Detection and categorization of bacteria habitats using shallow linguistic analysis Karadeniz, İlknur; Özgür, Arzucan. - : BioMed Central, 2015 BASE Show details

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