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1
What do You Mean by Relation Extraction? A Survey on Datasets and Study on Scientific Relation Classification ...
Bassignana, Elisa; Plank, Barbara. - : arXiv, 2022
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2
How Universal is Genre in Universal Dependencies? ...
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3
Universal Dependencies 2.9
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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4
Universal Dependencies 2.8.1
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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5
Universal Dependencies 2.8
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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6
On the Effectiveness of Dataset Embeddings in Mono-lingual,Multi-lingual and Zero-shot Conditions ...
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7
Genre as Weak Supervision for Cross-lingual Dependency Parsing ...
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8
Cross-Lingual Cross-Domain Nested Named Entity Evaluation on English Web Texts ...
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9
SemEval 2021 Task 12: Learning with Disagreement ...
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10
Genre as Weak Supervision for Cross-lingual Dependency Parsing ...
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11
DaN+: Danish Nested Named Entities and Lexical Normalization ...
Abstract: This paper introduces DaN+, a new multi-domain corpus and annotation guidelines for Danish nested named entities (NEs) and lexical normalization to support research on cross-lingual cross-domain learning for a less-resourced language. We empirically assess three strategies to model the two-layer Named Entity Recognition (NER) task. We compare transfer capabilities from German versus in-language annotation from scratch. We examine language-specific versus multilingual BERT, and study the effect of lexical normalization on NER. Our results show that 1) the most robust strategy is multi-task learning which is rivaled by multi-label decoding, 2) BERT-based NER models are sensitive to domain shifts, and 3) in-language BERT and lexical normalization are the most beneficial on the least canonical data. Our results also show that an out-of-domain setup remains challenging, while performance on news plateaus quickly. This highlights the importance of cross-domain evaluation of cross-lingual transfer. ... : COLING 2020 ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.2105.11301
https://arxiv.org/abs/2105.11301
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12
From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding ...
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13
SemEval-2021 Task 12: Learning with Disagreements
Uma, Alexandra; Fornaciari, Tommaso; Dumitrache, Anca. - : Association for Computational Linguistics, 2021
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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
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15
Universal Dependencies 2.7
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020
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16
Universal Dependencies 2.6
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020
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17
Universal Dependencies 2.5
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2019
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18
Universal Dependencies 2.4
Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2019
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19
Psycholinguistics meets Continual Learning: Measuring Catastrophic Forgetting in Visual Question Answering ...
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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
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