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Open-domain targeted sentiment analysis aims to detect opinion targets along with their sentiment polarities from a sentence.
Edge and curve detection for visual scene analysis
Azriel Rosenfeld and Mark Thurston. 1971 · 1971
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Conditional random fields: Probabilistic models for segmenting and labeling sequence data
John Lafferty, Andrew McCallum, and Fernando CN Pereira. 2001 · 2001
Earlier work this paper cites.
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Bo Pang, Lillian Lee, et al. 2008 · 2008
Earlier work this paper cites.
Joint sentiment/topic model for sentiment analysis
Chenghua Lin and Yulan He. 2009 · 2009
Earlier work this paper cites.
Extracting opinion targets in a single-and cross-domain setting with conditional random fields
Niklas Jakob and Iryna Gurevych. 2010 · 2010
Earlier work this paper cites.
Target-dependent twitter sentiment classification
Long Jiang, Mo Yu, Ming Zhou, Xiaohua Liu, and Tiejun Zhao. 2011 · 2011
Earlier work this paper cites.
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Bing Liu. 2012 · 2012
Earlier work this paper cites.
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Margaret Mitchell, Jacqui Aguilar, Theresa Wilson, and Benjamin Van Durme. 2013 · 2013
Earlier work this paper cites.
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Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
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Li Dong, Furu Wei, Chuanqi Tan, Duyu Tang, Ming Zhou, and Ke Xu. 2014 · 2014
Earlier work this paper cites.
Convolutional neural networks for sentence classification
Yoon Kim. 2014 · 2014
Earlier work this paper cites.
Semeval-2014 task 4: Aspect based sentiment analysis
Maria Pontiki, Dimitris Galanis, John Pavlopoulos, Harris Papageorgiou, Ion Androutsopoulos, and Suresh Manandhar. 2014 · 2014
Earlier work this paper cites.
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Pengfei Liu, Shafiq Joty, and Helen Meng. 2015 · 2015
Cited alongside, same era.
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Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, Suresh Manandhar, and Ion Androutsopoulos. 2015 · 2015
Cited alongside, same era.
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Meishan Zhang, Yue Zhang, and Duy Tin Vo. 2015 · 2015
Cited alongside, same era.
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Kenton Lee, Shimi Salant, Tom Kwiatkowski, Ankur Parikh, Dipanjan Das, and Jonathan Berant. 2016 · 2016
Cited alongside, same era.
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Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, Ion Androutsopoulos, Suresh Manandhar, AL-Smadi Mohammad, Mahmoud Al-Ayyoub, Yanyan Zhao, Bing Qin, Orphée De Clercq, et al. 2016 · 2016
Cited alongside, same era.
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Lei Shu, Hu Xu, and Bing Liu. 2017 · 2017
Later among the works it cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Later among the works it cites.
Machine comprehension using match-lstm and answer pointer
Shuohang Wang and Jing Jiang. 2017 · 2017
Later among the works it cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Later among the works it cites.
Multi-grained attention network for aspect-level sentiment classification
Feifan Fan, Yansong Feng, and Dongyan Zhao. 2018 · 2018
Later among the works it cites.
Jointly predicting predicates and arguments in neural semantic role labeling
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Later among the works it cites.
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Later among the works it cites.
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