Dheeraj Rajagopal
Dheeraj Rajagopal
Graduate Student
Dirección de correo verificada de cs.cmu.edu - Página principal
Citado por
Citado por
SenticNet 3: a common and common-sense knowledge base for cognition-driven sentiment analysis
E Cambria, D Olsher, D Rajagopal
Proceedings of the twenty-eighth AAAI conference on artificial intelligence …, 2014
Big social data analysis
E Cambria, D Rajagopal, D Olsher, D Das
Big data computing 13, 401-414, 2013
A graph-based approach to commonsense concept extraction and semantic similarity detection
D Rajagopal, E Cambria, D Olsher, K Kwok
Proceedings of the 22nd International Conference on World Wide Web, 565-570, 2013
Gated-attention architectures for task-oriented language grounding
DS Chaplot, KM Sathyendra, RK Pasumarthi, D Rajagopal, ...
arXiv preprint arXiv:1706.07230, 2017
Generating questions and multiple-choice answers using semantic analysis of texts
J Araki, D Rajagopal, S Sankaranarayanan, S Holm, Y Yamakawa, ...
Proceedings of COLING 2016, the 26th International Conference on …, 2016
Commonsense-based topic modeling
D Rajagopal, D Olsher, E Cambria, K Kwok
Proceedings of the second international workshop on issues of sentiment …, 2013
Simple and effective semi-supervised question answering
B Dhingra, D Pruthi, D Rajagopal
arXiv preprint arXiv:1804.00720, 2018
GECKA: game engine for commonsense knowledge acquisition
E Cambria, D Rajagopal, K Kwok, J Sepulveda
The Twenty-Eighth International Flairs Conference, 2015
StructSum: Incorporating Latent and Explicit Sentence Dependencies for Single Document Summarization
V Balachandran, A Pagnoni, JY Lee, D Rajagopal, J Carbonell, ...
arXiv preprint arXiv:2003.00576, 2020
Domain adaptation of srl systems for biological processes
D Rajagopal, N Vyas, A Siddhant, A Rayasam, N Tandon, E Hovy
Proceedings of the 18th BioNLP Workshop and Shared Task, 80-87, 2019
A common and common-sense knowledge base for cognition-driven sentiment analysis.
E Cambria, D Olsher, D Rajagopal
Proc. of the 28 AAAI conference on artificial intelligence, 2014
Markov chains for robust graph-based commonsense information extraction
N Tandon, D Rajagopal, G de Melo
Proceedings of COLING 2012: Demonstration Papers, 439-446, 2012
What-if I ask you to explain: Explaining the effects of perturbations in procedural text
D Rajagopal, N Tandon, P Clarke, B Dalvi, E Hovy
arXiv preprint arXiv:2005.01526, 2020
Unsupervised event coreference for abstract words
D Rajagopal, E Hovy, T Mitamura
Proceedings of the Workshop on Uphill Battles in Language Processing …, 2016
A proposal of the marriage of encyclopedic and commonsense knowledge
D Rajagopal, N Tandon
CMU LTI-SRS symposium.(cited on page 8), 2015
A Dataset for Tracking Entities in Open Domain Procedural Text
N Tandon, K Sakaguchi, BD Mishra, D Rajagopal, P Clark, M Guerquin, ...
arXiv preprint arXiv:2011.08092, 2020
EIGEN: Event Influence GENeration using Pre-trained Language Models
A Madaan, D Rajagopal, Y Yang, A Ravichander, E Hovy, S Prabhumoye
arXiv preprint arXiv:2010.11764, 2020
Modeling the Relationship between User Comments and Edits in Document Revision
X Zhang, D Rajagopal, M Gamon, SK Jauhar, CT Lu
Proceedings of the 2019 Conference on Empirical Methods in Natural Language …, 2019
Learning to Define Terms in the Software Domain
V Balachandran, D Rajagopal, RC Kanjirathinkal, W Cohen
Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User …, 2018
Gated-Attention Architectures for Task-Oriented Language Grounding
D Singh Chaplot, K Mysore Sathyendra, RK Pasumarthi, D Rajagopal, ...
arXiv, arXiv: 1706.07230, 2017
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