Fetching the paper…
Reading the bibliography…
This paper describes the system submitted by ANA Team for the SemEval-2019 Task 3: EmoContext.
Current state of text sentiment analysis from opinion to emotion mining
Ali Yaddolahi, Ameneh Gholipour Shahraki, and Osmar R. Zaiane. 2017 · 1904
Earlier work this paper cites.
An argument for basic emotions
Paul Ekman. 1992 · 1992
Earlier work this paper cites.
The nature of emotions: Human emotions have deep evolutionary roots, a fact that may explain their complexity and provide tools for clinical practice
Robert Plutchik. 2001 · 2001
Earlier work this paper cites.
Machine learning in non-stationary environments: Introduction to covariate shift adaptation
Masashi Sugiyama and Motoaki Kawanabe. 2012 · 2012
Earlier work this paper cites.
Crowdsourcing a word–emotion association lexicon
Saif M Mohammad and Peter D Turney. 2013 · 2013
Earlier work this paper cites.
Sentiment analysis of short informal texts
Svetlana Kiritchenko, Xiaodan Zhu, and Saif M Mohammad. 2014 · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
Earlier work this paper cites.
A hierarchical recurrent encoder-decoder for generative context-aware query suggestion
Alessandro Sordoni, Yoshua Bengio, Hossein Vahabi, Christina Lioma, Jakob Grue Simonsen, and Jian-Yun Nie. 2015 · 2015
Cited alongside, same era.
Building end-to-end dialogue systems using generative hierarchical neural network models
Iulian V Serban, Alessandro Sordoni, Yoshua Bengio, Aaron Courville, and Joelle Pineau. 2016 · 2016
Cited alongside, same era.
Datastories at semeval-2017 task 4: Deep lstm with attention for message-level and topic-based sentiment analysis
Christos Baziotis, Nikos Pelekis, and Christos Doulkeridis. 2017 · 2017
Cited alongside, same era.
Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm
Bjarke Felbo, Alan Mislove, Anders Søgaard, Iyad Rahwan, and Sune Lehmann. 2017 · 2017
Cited alongside, same era.
A structured self-attentive sentence embedding
Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio. 2017 · 2017
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.
Automatic dialogue generation with expressed emotions
Chenyang Huang, Osmar R. Zaiane, Amine Trabelsi, and Nouha Dziri. 2018 · 2018
Later among the works it cites.
Semeval-2018 task 1: Affect in tweets
Saif Mohammad, Felipe Bravo-Marquez, Mohammad Salameh, and Svetlana Kiritchenko. 2018 · 2018
Later among the works it cites.
Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Later among the works it cites.
Semeval-2019 task 3: Emocontext: Contextual emotion detection in text
Ankush Chatterjee, Kedhar Nath Narahari, Meghana Joshi, and Puneet Agrawal. 2019b · 2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
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
Cited alongside, same era.
Understanding emotions in text using deep learning and big data
Ankush Chatterjee, Umang Gupta, Manoj Kumar Chinnakotla, Radhakrishnan Srikanth, Michel Galley, and Puneet Agrawal. 2019a
Cited in the paper.
Semeval19 task 3: Emocontext
CodaLab. 2019 · 2019
Closest in time.