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Hits 21 – 40 of 69

21
Universal Dependencies 2.7
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020
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22
Universal Dependencies 2.6
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020
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23
It's not Greek to mBERT: Inducing Word-Level Translations from Multilingual BERT ...
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24
The Extraordinary Failure of Complement Coercion Crowdsourcing ...
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25
Amnesic Probing: Behavioral Explanation with Amnesic Counterfactuals ...
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26
Break It Down: A Question Understanding Benchmark ...
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27
Unsupervised Domain Clusters in Pretrained Language Models ...
Aharoni, Roee; Goldberg, Yoav. - : arXiv, 2020
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28
Nakdan: Professional Hebrew Diacritizer ...
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29
Interactive Extractive Search over Biomedical Corpora ...
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30
Unsupervised Distillation of Syntactic Information from Contextualized Word Representations ...
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31
pyBART: Evidence-based Syntactic Transformations for IE ...
Abstract: Syntactic dependencies can be predicted with high accuracy, and are useful for both machine-learned and pattern-based information extraction tasks. However, their utility can be improved. These syntactic dependencies are designed to accurately reflect syntactic relations, and they do not make semantic relations explicit. Therefore, these representations lack many explicit connections between content words, that would be useful for downstream applications. Proposals like English Enhanced UD improve the situation by extending universal dependency trees with additional explicit arcs. However, they are not available to Python users, and are also limited in coverage. We introduce a broad-coverage, data-driven and linguistically sound set of transformations, that makes event-structure and many lexical relations explicit. We present pyBART, an easy-to-use open-source Python library for converting English UD trees either to Enhanced UD graphs or to our representation. The library can work as a standalone package or ... : Accepted ACL2020 system demonstration paper ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.2005.01306
https://arxiv.org/abs/2005.01306
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32
Syntactic Search by Example ...
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33
Universal Dependencies 2.5
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2019
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34
Universal Dependencies 2.4
Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2019
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35
Ab Antiquo: Neural Proto-language Reconstruction ...
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36
How does Grammatical Gender Affect Noun Representations in Gender-Marking Languages? ...
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37
Where’s My Head? Definition, Data Set, and Models for Numeric Fused-Head Identification and Resolution
In: Transactions of the Association for Computational Linguistics, Vol 7, Pp 519-535 (2019) (2019)
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38
Universal Dependencies 2.2
In: https://hal.archives-ouvertes.fr/hal-01930733 ; 2018 (2018)
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39
Universal Dependencies 2.3
Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2018
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40
Universal Dependencies 2.2
Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2018
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