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Hits 1 – 11 of 11
1
Don't Let Discourse Confine Your Model: Sequence Perturbations for Improved Event Language Models ...
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
;
Balasubramanian, Niranjan
;
Chambers, Nathanael
. - : Underline Science Inc., 2021
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2
TellMeWhy: A Dataset for Answering Why-Questions in Narratives ...
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
;
Balasubramanian, Niranjan
;
Chambers, Nathanael
. - : Underline Science Inc., 2021
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3
Conditional Generation of Temporally-ordered Event Sequences ...
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
;
Chambers, Nathanael
;
Durrett, Greg
. - : Underline Science Inc., 2021
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4
Modeling Label Semantics for Predicting Emotional Reactions ...
Gaonkar, Radhika
;
Kwon, Heeyoung
;
Bastan, Mohaddeseh
. - : arXiv, 2020
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5
Advances in statistical script learning
Pichotta, Karl
. - 2017
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6
USNA: A Dual-Classifier Approach to Contextual Sentiment Analysis
Harihara, Ganesh
;
Yang, Eugene
;
Chambers, Nathanael
In: DTIC (2013)
Abstract:
This paper describes a dual-classifier approach to contextual sentiment analysis at the SemEval-2013 Task 2. Contextual analysis of polarity focuses on a word or phrase, rather than the broader task of identifying the sentiment of an entire text. The Task 2 definition includes target word spans that range in size from a single word to entire sentences. However the context of a single word is dependent on the word's surrounding syntax, while a phrase contains most of the polarity within itself. We thus describe separate treatment with two independent classifiers, outperforming the accuracy of a single classifier. Our system ranked 6th out of 19 teams on SMS message classification, and 8th of 23 on twitter data. We also show a surprising result that a very small amount of word context is needed for high-performance polarity extraction. ; See also ADA587003. Presented at the SEM 2013: The Second Joint Conference on Lexical and Computational Semantics, Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013) Held in Atlanta, Georgia on June 14-15, 2013. Sponsored in part by Defense Advanced Research Projects Agency \201DARPA\202.
Keyword:
*WORDS(LANGUAGE)
;
CLASSIFICATION
;
EXTRACTION
;
Linguistics
;
PHRASE STRUCTURE GRAMMARS
URL:
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA587816
http://www.dtic.mil/docs/citations/ADA587816
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7
NavyTime: Event and Time Ordering from Raw Text
Chambers, Nathanael
In: DTIC (2013)
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8
Learning for Microblogs with Distant Supervision: Political Forecasting with Twitter
Marchetti-Bowick, Micol
;
Chambers, Nathanael
In: DTIC (2012)
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9
Labeling Documents with Timestamps: Learning from their Time Expressions
Chambers, Nathanael
In: DTIC (2012)
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10
Improving the use of pseudo-words for evaluating selectional preferences
Chambers, Nathanael
;
Jurafsky, Dan
In:
Association for Computational Linguistics. Proceedings of the conference. - Stroudsburg, Penn. : ACL
48 (2010) 1, 445-453
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11
Stochastic Language Generation in a Dialogue System: Toward a Domain Independent Generator
Chambers, Nathanael
;
Allen, James
In: DTIC (2004)
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