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A Semi-Supervised Approach to Improve Classification of Infrequent Discourse Relations Using Feature Vector Extension
Hugo Hernault
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Danushka Bollegala
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Mitsuru Ishizuka
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Paper Details:
Month: October
Year: 2010
Location: Cambridge, MA
Venue:
EMNLP |
SIG: SIGDAT
Citations
URL
Training Data Enrichment for Infrequent Discourse Relations
Kailang Jiang
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Giuseppe Carenini
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Raymond Ng
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Recursive Deep Models for Discourse Parsing
Jiwei Li
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Rumeng Li
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Eduard Hovy
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Do We Need Cross Validation for Discourse Relation Classification?
Wei Shi
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Vera Demberg
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Using Explicit Discourse Connectives in Translation for Implicit Discourse Relation Classification
Wei Shi
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Frances Yung
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Raphael Rubino
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Vera Demberg
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Text-level Discourse Parsing with Rich Linguistic Features
Vanessa Wei Feng
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Graeme Hirst
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Leveraging Synthetic Discourse Data via Multi-task Learning for Implicit Discourse Relation Recognition
Man Lan
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Yu Xu
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Zhengyu Niu
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Text-level Discourse Dependency Parsing
Sujian Li
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Liang Wang
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Ziqiang Cao
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Wenjie Li
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Are ACT’s Scores Increasing with Better Translation Quality?
Najeh Hajlaoui
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Addressing Class Imbalance for Improved Recognition of Implicit Discourse Relations
Junyi Jessy Li
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Ani Nenkova
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Reducing Sparsity Improves the Recognition of Implicit Discourse Relations
Junyi Jessy Li
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Ani Nenkova
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Coherence Modeling Improves Implicit Discourse Relation Recognition
Noriki Nishida
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Hideki Nakayama
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http://en.wikipedia.org
Field Of Study
Linguistic Trends
Discourse
Task
Discourse Parsing
Information Retrieval
Sentiment Analysis
Summarization
Language
English
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