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Question-answering plays an important role in e-commerce as it allows potential customers to actively seek crucial information about products or services to help their purchase decision making.
Scaling question answering to the web
Cody Kwok, Oren Etzioni, and Daniel S Weld. 2001 · 2001
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Thumbs up?: sentiment classification using machine learning techniques
Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan. 2002 · 2002
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Mining and summarizing customer reviews
Minqing Hu and Bing Liu. 2004 · 2004
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Scaling up question-answering to linked data
Vanessa Lopez, Andriy Nikolov, Marta Sabou, Victoria Uren, Enrico Motta, and Mathieu d’Aquin. 2010 · 2010
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Sentiment analysis and opinion mining
Bing Liu. 2012 · 2012
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Template-based question answering over rdf data
Christina Unger, Lorenz Bühmann, Jens Lehmann, Axel-Cyrille Ngonga Ngomo, Daniel Gerber, and Philipp Cimiano. 2012 · 2012
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
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Mctest: A challenge dataset for the open-domain machine comprehension of text
Matthew Richardson, Christopher JC Burges, and Erin Renshaw. 2013 · 2013
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Open question answering over curated and extracted knowledge bases
Anthony Fader, Luke Zettlemoyer, and Oren Etzioni. 2014 · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
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Information extraction over structured data: Question answering with freebase
Xuchen Yao and Benjamin Van Durme. 2014 · 2014
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Question answering over freebase with multi-column convolutional neural networks
Li Dong, Furu Wei, Ming Zhou, and Ke Xu. 2015 · 2015
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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
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The goldilocks principle: Reading children’s books with explicit memory representations
Felix Hill, Antoine Bordes, Sumit Chopra, and Jason Weston. 2015 · 2015
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Sentiment analysis: Mining opinions, sentiments, and emotions
Bing Liu. 2015 · 2015
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Neural generative question answering
Jun Yin, Xin Jiang, Zhengdong Lu, Lifeng Shang, Hang Li, and Xiaoming Li. 2015 · 2015
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Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering
Ruining He and Julian McAuley. 2016 · 2016
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Wikireading: A novel large-scale language understanding task over wikipedia
Daniel Hewlett, Alexandre Lacoste, Llion Jones, Illia Polosukhin, Andrew Fandrianto, Jay Han, Matthew Kelcey, and David Berthelot. 2016 · 2016
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Addressing complex and subjective product-related queries with customer reviews
Julian McAuley and Alex Yang. 2016 · 2016
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Ms marco: A human generated machine reading comprehension dataset
Coupled multi-layer attentions for co-extraction of aspect and opinion terms
Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier, and Xiaokui Xiao. 2017 · 2017
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Quac: Question answering in context
Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, and Luke Zettlemoyer. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Exploiting document knowledge for aspect-level sentiment classification
Ruidan He, Wee Sun Lee, Hwee Tou Ng, and Daniel Dahlmeier. 2018 · 2018
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Universal language model fine-tuning for text classification
Jeremy Howard and Sebastian Ruder. 2018 · 2018
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Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016 · 2016
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Aspect level sentiment classification with deep memory network
Duyu Tang, Bing Qin, and Ting Liu. 2016 · 2016
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Newsqa: A machine comprehension dataset
Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman. 2016 · 2016
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Recursive neural conditional random fields for aspect-based sentiment analysis
Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier, and Xiaokui Xiao. 2016 · 2016
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Question answering on freebase via relation extraction and textual evidence
Kun Xu, Siva Reddy, Yansong Feng, Songfang Huang, and Dongyan Zhao. 2016 · 2016
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Reading wikipedia to answer open-domain questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017 · 2017
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Searchqa: A new q&a dataset augmented with context from a search engine
Matthew Dunn, Levent Sagun, Mike Higgins, V Ugur Guney, Volkan Cirik, and Kyunghyun Cho. 2017 · 2017
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The narrativeqa reading comprehension challenge
Tomáš Kočiskỳ, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gáabor Melis, and Edward Grefenstette. 2018 · 2018
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Exploiting coarse-to-fine task transfer for aspect-level sentiment classification
Zheng Li, Ying Wei, Yu Zhang, Xiang Zhang, Xin Li, and Qiang Yang. 2018 · 2018
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Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Know what you don’t know: Unanswerable questions for squad
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
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Coqa: A conversational question answering challenge
Siva Reddy, Danqi Chen, and Christopher D Manning. 2018 · 2018
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Drcd: a chinese machine reading comprehension dataset
Chih Chieh Shao, Trois Liu, Yuting Lai, Yiying Tseng, and Sam Tsai. 2018 · 2018
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Constructing datasets for multi-hop reading comprehension across documents
Johannes Welbl, Pontus Stenetorp, and Sebastian Riedel. 2018 · 2018
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Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W Cohen, Ruslan Salakhutdinov, and Christopher D Manning. 2018 · 2018
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Aware answer prediction for product-related questions incorporating aspects
Qian Yu and Wai Lam. 2018 · 2018
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