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Do We Really Need All Those Rich Linguistic Features? A Neural Network-Based Approach to Implicit Sense Labeling
Niko Schenk
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Christian Chiarcos
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Kathrin Donandt
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Samuel Rönnqvist
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Evgeny Stepanov
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Giuseppe Riccardi
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Paper Details:
Month: August
Year: 2016
Location: Berlin, Germany
Venue:
CoNLL |
SIG: SIGNLL
Citations
URL
A Knowledge-Augmented Neural Network Model for Implicit Discourse Relation Classification
Yudai Kishimoto
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Yugo Murawaki
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Sadao Kurohashi
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A Systematic Study of Neural Discourse Models for Implicit Discourse Relation
Attapol Rutherford
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Vera Demberg
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Nianwen Xue
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CoNLL 2016 Shared Task on Multilingual Shallow Discourse Parsing
Nianwen Xue
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Hwee Tou Ng
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Sameer Pradhan
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Attapol Rutherford
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Bonnie Webber
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Chuan Wang
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Hongmin Wang
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A Recurrent Neural Model with Attention for the Recognition of Chinese Implicit Discourse Relations
Samuel Rönnqvist
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Niko Schenk
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Christian Chiarcos
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Argument Labeling of Explicit Discourse Relations using LSTM Neural Networks
Sohail Hooda
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Leila Kosseim
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Resource-Lean Modeling of Coherence in Commonsense Stories
Niko Schenk
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Christian Chiarcos
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http://www.cs.brandeis.edu/
https://github.com/
http://www.cs.brandeis.edu/
http://radimrehurek.com/gensim/
https://taku910.github.io/crfpp/
http://ilk.uvt.nl/team/sabine/chunklink/
https://www.csie.ntu.edu.tw/
Field Of Study
Linguistic Trends
Discourse
Lexical Semantics
Embeddings
Ontologies
Syntax
Task
Language Understanding
Tagging
Discourse Parsing
Approach
Deep Learning
Language
Chinese
English
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