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1
Universal Dependencies 2.9
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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2
Universal Dependencies 2.8.1
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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3
Universal Dependencies 2.8
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2021
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4
Anonymisation Models for Text Data: State of the art, Challenges and Future Directions ...
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5
Structured Sentiment Analysis as Dependency Graph Parsing ...
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6
Universal Dependencies 2.7
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020
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7
Universal Dependencies 2.6
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2020
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8
Gender and sentiment, critics and authors: a dataset of Norwegian book reviews ...
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9
Building a Norwegian Lexical Resource for Medical Entity Recognition ...
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10
Universal Dependencies 2.5
Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell. - : Universal Dependencies Consortium, 2019
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11
Universal Dependencies 2.4
Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2019
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12
Probing Multilingual Sentence Representations With X-Probe ...
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13
Sentiment analysis is not solved! Assessing and probing sentiment classification ...
Abstract: Neural methods for SA have led to quantitative improvements over previous approaches, but these advances are not always accompanied with a thorough analysis of the qualitative differences. Therefore, it is not clear what outstanding conceptual challenges for sentiment analysis remain. In this work, we attempt to discover what challenges still prove a problem for sentiment classifiers for English and to provide a challenging dataset. We collect the subset of sentences that an (oracle) ensemble of state-of-the-art sentiment classifiers misclassify and then annotate them for 18 linguistic and paralinguistic phenomena, such as negation, sarcasm, modality, etc. The dataset is available at https://github.com/ltgoslo/assessing_and_probing_sentiment. Finally, we provide a case study that demonstrates the usefulness of the dataset to probe the performance of a given sentiment classifier with respect to linguistic phenomena. ... : Accepted to BlackBoxNLP Workshop at ACL 2019 ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/1906.05887
https://dx.doi.org/10.48550/arxiv.1906.05887
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14
Universal Dependencies 2.2
In: https://hal.archives-ouvertes.fr/hal-01930733 ; 2018 (2018)
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15
Universal Dependencies 2.3
Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2018
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16
Universal Dependencies 2.2
Nivre, Joakim; Abrams, Mitchell; Agić, Željko. - : Universal Dependencies Consortium, 2018
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17
Diachronic word embeddings and semantic shifts: a survey ...
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18
Universal Dependencies 2.1
In: https://hal.inria.fr/hal-01682188 ; 2017 (2017)
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19
Universal Dependencies 2.0 alpha (obsolete)
Nivre, Joakim; Agić, Željko; Ahrenberg, Lars. - : Universal Dependencies Consortium, 2017
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20
Universal Dependencies 2.0
Nivre, Joakim; Agić, Željko; Ahrenberg, Lars. - : Universal Dependencies Consortium, 2017
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