Jordan Boyd-Graber - Google Scholar

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Citations	7127	5550
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Philip ResnikProfessor, Linguistics and UMIACS, University of MarylandVerified email at umd.edu
Mohit IyyerAssistant Professor, University of Massachusetts AmherstVerified email at cs.umass.edu
Hal Daumé IIIAssociate Professor of Computer Science, University of MarylandVerified email at umiacs.umd.edu
David BleiProfessor of Statistics and Computer Science, Columbia UniversityVerified email at columbia.edu
Yuening HuComputer Science, University of Maryland, College ParkVerified email at cs.umd.edu
Alison M Smith-RennerComputer Science PhD, University of MarylandVerified email at cs.umd.edu
Leah FindlaterAssociate Professor, University of WashingtonVerified email at uw.edu
Jonathan ChangFacebookVerified email at fb.com
Ke ZhaiUniversity of Maryland, College ParkVerified email at umd.edu
Viet-An NguyenData Scientist, FacebookVerified email at fb.com
Kevin SeppiProfessor of Computer Science, Brigham Young UniversityVerified email at cs.byu.edu
He HeNew York UniversityVerified email at cs.nyu.edu
Varun ManjunathaResearch Scientist, Adobe ResearchVerified email at cs.umd.edu
Shi FengUniversity of Maryland, College ParkVerified email at cs.umd.edu
Leonardo ClaudinoHCPS-NINDS-NIHVerified email at nih.gov
Alvin Grissom IIAssistant Professor, Haverford CollegeVerified email at haverford.edu
Chong WangHead of AML Research at ByteDanceVerified email at cs.princeton.edu
Sean GerrishSoftware Engineer, Google Inc.Verified email at cs.princeton.edu
Eric WallaceUC BerkeleyVerified email at berkeley.edu
Thang NguyenComputer Science, CLIP@Umiacs, Univerisity of MarylandVerified email at umiacs.umd.edu

Jordan Boyd-Graber

Associate Professor, University of Maryland

Verified email at umiacs.umd.edu - Homepage

Machine Learning Computational Linguistics Natural Language Processing Topic Models Question Answering


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Reading tea leaves: How humans interpret topic models J Chang, J Boyd-Graber, S Gerrish, C Wang, DM Blei Neural Information Processing Systems (NIPS) 31, 2009	1845	2009
Deep unordered composition rivals syntactic methods for text classification M Iyyer, V Manjunatha, J Boyd-Graber, H Daumé III Proceedings of the 53rd annual meeting of the association for computational …, 2015	548	2015
A neural network for factoid question answering over paragraphs M Iyyer, J Boyd-Graber, L Claudino, R Socher, H Daumé III Proceedings of the 2014 conference on empirical methods in natural language …, 2014	350	2014
Interactive topic modeling Y Hu, J Boyd-Graber, B Satinoff, A Smith Machine learning 95 (3), 423-469, 2014	291	2014
A topic model for word sense disambiguation J Boyd-Graber, D Blei, X Zhu Conference on Empirical Methods in Natural Language Processing (EMNLP), 1024 …, 2007	283	2007
Syntactic topic models J Boyd-Graber, DM Blei Neural Information Processing Systems (NIPS), 2008	240	2008
Political ideology detection using recursive neural networks M Iyyer, P Enns, J Boyd-Graber, P Resnik Proceedings of the 52nd Annual Meeting of the Association for Computational …, 2014	201	2014
Multilingual topic models for unaligned text J Boyd-Graber, D Blei arXiv preprint arXiv:1205.2657, 2012	182	2012
Mr. LDA: A flexible large scale topic modeling package using variational inference in mapreduce K Zhai, J Boyd-Graber, N Asadi, ML Alkhouja Proceedings of the 21st international conference on World Wide Web, 879-888, 2012	171	2012
Adding dense, weighted connections to WordNet J Boyd-Graber, C Fellbaum, D Osherson, R Schapire Proceedings of the third international WordNet conference, 29-36, 2006	155	2006
Connections between the lines: augmenting social networks with text J Chang, J Boyd-Graber, DM Blei Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009	147	2009
Holistic sentiment analysis across languages: Multilingual supervised latent Dirichlet allocation J Boyd-Graber, P Resnik Proceedings of the 2010 Conference on Empirical Methods in Natural Language …, 2010	132	2010
Care and feeding of topic models: Problems, diagnostics, and improvements J Boyd-Graber, D Mimno, D Newman Handbook of mixed membership models and their applications 225255, 2014	123	2014
Opponent modeling in deep reinforcement learning H He, J Boyd-Graber, K Kwok, H Daumé III International conference on machine learning, 1804-1813, 2016	115	2016
Participatory design with proxies: developing a desktop-PDA system to support people with aphasia JL Boyd-Graber, SS Nikolova, KA Moffatt, KC Kin, JY Lee, LW Mackey, ... Proceedings of the SIGCHI Conference on Human Factors in Computing Systems …, 2006	109	2006
Beyond LDA: exploring supervised topic modeling for depression-related language in Twitter P Resnik, W Armstrong, L Claudino, T Nguyen, VA Nguyen, ... Proceedings of the 2nd Workshop on Computational Linguistics and Clinical …, 2015	105	2015
Applications of topic models JL Boyd-Graber, Y Hu, D Mimno now Publishers Incorporated, 2017	102	2017
Topic models for dynamic translation model adaptation V Eidelman, J Boyd-Graber, P Resnik Proceedings of the 50th Annual Meeting of the Association for Computational …, 2012	100	2012
Feuding Families and Former Friends: Unsupervised Learning for Dynamic Fictional Relationships M Iyyer, A Guha, S Chaturvedi, J Boyd-Graber, H Daumé III	82	2016
Pathologies of neural models make interpretations difficult S Feng, E Wallace, A Grissom II, M Iyyer, P Rodriguez, J Boyd-Graber arXiv preprint arXiv:1804.07781, 2018	75	2018

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