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Better Document-level Sentiment Analysis from RST Discourse Parsing
Parminder Bhatia
|
Yangfeng Ji
|
Jacob Eisenstein
|
Paper Details:
Month: September
Year: 2015
Location: Lisbon, Portugal
Venue:
EMNLP |
SIG: SIGDAT
Citations
URL
Multi-view and multi-task training of RST discourse parsers
Chloé Braud
|
Barbara Plank
|
Anders Søgaard
|
Implicit Discourse Relation Recognition with Context-aware Character-enhanced Embeddings
Lianhui Qin
|
Zhisong Zhang
|
Hai Zhao
|
Training Data Enrichment for Infrequent Discourse Relations
Kailang Jiang
|
Giuseppe Carenini
|
Raymond Ng
|
Effective LSTMs for Target-Dependent Sentiment Classification
Duyu Tang
|
Bing Qin
|
Xiaocheng Feng
|
Ting Liu
|
Aspect Level Sentiment Classification with Deep Memory Network
Duyu Tang
|
Bing Qin
|
Ting Liu
|
Neural Sentiment Classification with User and Product Attention
Huimin Chen
|
Maosong Sun
|
Cunchao Tu
|
Yankai Lin
|
Zhiyuan Liu
|
Cached Long Short-Term Memory Neural Networks for Document-Level Sentiment Classification
Jiacheng Xu
|
Danlu Chen
|
Xipeng Qiu
|
Xuanjing Huang
|
Deep Neural Networks with Massive Learned Knowledge
Zhiting Hu
|
Zichao Yang
|
Ruslan Salakhutdinov
|
Eric Xing
|
Cross-lingual RST Discourse Parsing
Chloé Braud
|
Maximin Coavoux
|
Anders Søgaard
|
Neural Discourse Structure for Text Categorization
Yangfeng Ji
|
Noah A. Smith
|
Multiple Instance Learning Networks for Fine-Grained Sentiment Analysis
Stefanos Angelidis
|
Mirella Lapata
|
Learning Structured Text Representations
Yang Liu
|
Mirella Lapata
|
How can NLP Tasks Mutually Benefit Sentiment Analysis? A Holistic Approach to Sentiment Analysis
Lingjia Deng
|
Janyce Wiebe
|
A Joint Model of Rhetorical Discourse Structure and Summarization
Naman Goyal
|
Jacob Eisenstein
|
https://github.com/
https://github.com/jiyfeng/DPLP
http://www.sfu.ca/
Field Of Study
Linguistic Trends
Discourse
Task
Discourse Parsing
Sentiment Analysis
Summarization
Social Science
Approach
Bayesian Model
Structured Prediction
Representation Learning
Language
English
Dataset
Social Media
Twitter
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