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Simple Search
Hits 1 – 12 of 12
1
Neural Coreference Resolution for Arabic ...
The 28th International Conference on Computational Linguistics 2020
;
Aloraini, Abdulrahman
;
Poesio, Massimo
. - : Underline Science Inc., 2020
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2
Free the Plural: Unrestricted Split-Antecedent Anaphora Resolution ...
The 28th International Conference on Computational Linguistics 2020
;
Moosavi, Nafise Sadat
;
Paun, Silviu
. - : Underline Science Inc., 2020
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3
Phrase Detectives Corpus Version 2
Chamberlain, Jon
;
Paun, Silviu
;
Yu, Juntao
. - : Linguistic Data Consortium, 2019. : https://www.ldc.upenn.edu, 2019
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4
Phrase Detectives Corpus Version 2 ...
Chamberlain, Jon
;
Paun, Silviu
;
Yu, Juntao
. - : Linguistic Data Consortium, 2019
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5
Crowdsourcing and Aggregating Nested Markable Annotations ...
Madge, Chris
;
Yu, Juntao
;
Chamberlain, Jon
. - : Universität Regensburg, 2019
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6
Crowdsourcing and Aggregating Nested Markable Annotations
Madge, Chris
;
Yu, Juntao
;
Chamberlain, Jon
. - : Association for Computational Linguistics, 2019
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7
A Crowdsourced Corpus of Multiple Judgments and Disagreement on Anaphoric Interpretation
Paun, Silviu
;
Uma, Alexandra
;
Poesio, Massimo
. - : Association for Computational Linguistics, 2019
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8
Crowdsourcing and Aggregating Nested Markable Annotations
Poesio, Massimo
;
Yu, Juntao
;
Chamberlain, Jon
. - : Association for Computational Linguistics, 2019
BASE
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9
A Crowdsourced Corpus of Multiple Judgments and Disagreement on Anaphoric Interpretation
Poesio, Massimo
;
Chamberlain, Jon
;
Paun, Silviu
. - : Association for Computational Linguistics, 2019
BASE
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10
Semi-Supervised Methods for Out-of-Domain Dependency Parsing ...
Yu, Juntao
. - : arXiv, 2018
Abstract:
Dependency parsing is one of the important natural language processing tasks that assigns syntactic trees to texts. Due to the wider availability of dependency corpora and improved parsing and machine learning techniques, parsing accuracies of supervised learning-based systems have been significantly improved. However, due to the nature of supervised learning, those parsing systems highly rely on the manually annotated training corpora. They work reasonably good on the in-domain data but the performance drops significantly when tested on out-of-domain texts. To bridge the performance gap between in-domain and out-of-domain, this thesis investigates three semi-supervised techniques for out-of-domain dependency parsing, namely co-training, self-training and dependency language models. Our approaches use easily obtainable unlabelled data to improve out-of-domain parsing accuracies without the need of expensive corpora annotation. The evaluations on several English domains and multi-lingual data show quite good ... : PhD Thesis ...
Keyword:
Computation and Language cs.CL
;
FOS Computer and information sciences
URL:
https://arxiv.org/abs/1810.02100
https://dx.doi.org/10.48550/arxiv.1810.02100
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11
Semi-supervised methods for out-of-domain dependency parsing
Yu, Juntao
. - 2018
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12
A Probabilistic Annotation Model for Crowdsourcing Coreference
Kruschwitz, Udo
;
Chamberlain, Jon
;
Yu, Juntao
. - : Association for Computational Linguistics, 2018
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