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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 3 Hits 1 – 20 of 49 1 What do You Mean by Relation Extraction? A Survey on Datasets and Study on Scientific Relation Classification ... Bassignana, Elisa; Plank, Barbara. - : arXiv, 2022 BASE Show details 2 How Universal is Genre in Universal Dependencies? ... Müller-Eberstein, Max; van der Goot, Rob; Plank, Barbara. - : arXiv, 2021 BASE Show details 3 Universal Dependencies 2.9 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 4 Universal Dependencies 2.8.1 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 5 Universal Dependencies 2.8 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021 BASE Show details 6 On the Effectiveness of Dataset Embeddings in Mono-lingual,Multi-lingual and Zero-shot Conditions ... van der Goot, Rob; Üstün, Ahmet; Plank, Barbara. - : arXiv, 2021 Abstract: Recent complementary strands of research have shown that leveraging information on the data source through encoding their properties into embeddings can lead to performance increase when training a single model on heterogeneous data sources. However, it remains unclear in which situations these dataset embeddings are most effective, because they are used in a large variety of settings, languages and tasks. Furthermore, it is usually assumed that gold information on the data source is available, and that the test data is from a distribution seen during training. In this work, we compare the effect of dataset embeddings in mono-lingual settings, multi-lingual settings, and with predicted data source label in a zero-shot setting. We evaluate on three morphosyntactic tasks: morphological tagging, lemmatization, and dependency parsing, and use 104 datasets, 66 languages, and two different dataset grouping strategies. Performance increases are highest when the datasets are of the same language, and we know from ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2103.01273 https://arxiv.org/abs/2103.01273 BASE Hide details 7 Genre as Weak Supervision for Cross-lingual Dependency Parsing ... Müller-Eberstein, Max; van der Goot, Rob; Plank, Barbara. - : arXiv, 2021 BASE Show details 8 Cross-Lingual Cross-Domain Nested Named Entity Evaluation on English Web Texts ... 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; Plank, Barbara. - : Underline Science Inc., 2021 BASE Show details 9 SemEval 2021 Task 12: Learning with Disagreement ... 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; Chamberlain, Jon; Dumitrache, Anca. - : Underline Science Inc., 2021 BASE Show details 10 Genre as Weak Supervision for Cross-lingual Dependency Parsing ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Müller-Eberstein, Max; Plank, Barbara. - : Underline Science Inc., 2021 BASE Show details 11 DaN+: Danish Nested Named Entities and Lexical Normalization ... Plank, Barbara; Jensen, Kristian Nørgaard; van der Goot, Rob. - : arXiv, 2021 BASE Show details 12 From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding ... van der Goot, Rob; Sharaf, Ibrahim; Imankulova, Aizhan. - : arXiv, 2021 BASE Show details 13 SemEval-2021 Task 12: Learning with Disagreements Uma, Alexandra; Fornaciari, Tommaso; Dumitrache, Anca. - : Association for Computational Linguistics, 2021 BASE Show details 14 We Need to Consider Disagreement in Evaluation Basile, Valerio; Fell, Michael; Fornaciari, Tommaso. - : Association for Computational Linguistics, 2021. : country:USA, 2021. : place:Stroudsburg, PA, 2021 BASE Show details 15 Universal Dependencies 2.7 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020 BASE Show details 16 Universal Dependencies 2.6 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020 BASE Show details 17 Universal Dependencies 2.5 Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2019 BASE Show details 18 Universal Dependencies 2.4 Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2019 BASE Show details 19 Psycholinguistics meets Continual Learning: Measuring Catastrophic Forgetting in Visual Question Answering ... Greco, Claudio; Plank, Barbara; Fernández, Raquel. - : arXiv, 2019 BASE Show details 20 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 Page: 1 2 3

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