Alison Smith ------------------------- Alison Smith, Varun Kumar, Jordan Boyd-Graber, Kevin Seppi, and Leah Findlater. User-Centered Design and Evaluation of a Human-in-the-Loop Topic Modeling System. Intelligent User Interfaces, 2018.Alison won a best student paper honorable mention (3 out of 300) (23% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2018_iui_itm.pdf Alison Smith, Varun Kumar, Jordan Boyd-Graber, Kevin Seppi, and Leah Findlater. Accounting for Input Uncertainty in Human-in-the-Loop Systems. CHI 2017 Designing for Uncertainty Workshop, 2017. http://umiacs.umd.edu/~jbg/http://visualization.ischool.uw.edu/hci_uncertainty/papers/Paper11.pdf Tak Yeon Lee, Alison Smith, Kevin Seppi, Niklas Elmqvist, Jordan Boyd-Graber, and Leah Findlater. The Human Touch: How Non-expert Users Perceive, Interpret, and Fix Topic Models. International Journal of Human-Computer Studies, 2017. http://umiacs.umd.edu/~jbg/docs/2017_ijhcs_human_touch.pdf Alison Smith, Tak Yeon Lee, Forough Poursabzi-Sangdeh, Jordan Boyd-Graber, Kevin Seppi, Niklas Elmqvist, and Leah Findlater. Evaluating Visual Representations for Topic Understanding and Their Effects on Manually Generated Labels. Transactions of the Association for Computational Linguistics, 2017. http://umiacs.umd.edu/~jbg/docs/2017_tacl_eval_tm_viz.pdf Alison Smith, Tak Yeon Lee, Forough Poursabzi-Sangdeh, Jordan Boyd-Graber, Kevin Seppi, Niklas Elmqvist, and Leah Findlater. Human-Centered and Interactive: Expanding the Impact of Topic Models. CHI Human Centred Machine Learning Workshop, 2016. Alison Smith, Jason Chuang, Yuening Hu, Jordan Boyd-Graber, and Leah Findlater. Concurrent Visualization of Relationships between Words and Topics in Topic Models. ACL Workshop on Workshop on Interactive Language Learning, Visualization, and Interfaces, 2014. Yuening Hu, Jordan Boyd-Graber, Brianna Satinoff, and Alison Smith. Interactive Topic Modeling. Machine Learning, 2014. http://umiacs.umd.edu/~jbg/docs/2014_mlj_itm.pdf Alvin Grissom II ------------------------- Alvin Grissom II, Naho Orita, and Jordan Boyd-Graber. Incremental Prediction of Sentence-final Verbs. Conference on Computational Natural Language Learning, 2016. (20% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_conll_verbpred.pdf He He, Alvin Grissom II, Jordan Boyd-Graber, and Hal Daume III. Syntax-based Rewriting for Simultaneous Machine Translation. Empirical Methods in Natural Language Processing, 2015. (24% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_emnlp_rewrite.pdf Alvin Grissom II, He He, Jordan Boyd-Graber, John Morgan, and Hal Daume III. Don't Until the Final Verb Wait: Reinforcement Learning for Simultaneous Machine Translation. Empirical Methods in Natural Language Processing, 2014. (30% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_emnlp_simtrans.pdf Anupam Guha ------------------------- Mohit Iyyer, Varun Manjunatha, Anupam Guha, Yogarshi Vyas, Jordan Boyd-Graber, Hal Daume III, and Larry Davis. The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narratives. Computer Vision and Pattern Recognition, 2017. http://umiacs.umd.edu/~jbg/docs/2017_cvpr_comics.pdf Anupam Guha, Mohit Iyyer, and Jordan Boyd-Graber. A Distorted Skull Lies in the Bottom Center: Identifying Paintings from Text Descriptions. NAACL Human-Computer Question Answering Workshop, 2016. http://umiacs.umd.edu/~jbg/docs/2016_naacl_paintings.pdf Mohit Iyyer, Anupam Guha, Snigdha Chaturvedi, Jordan Boyd-Graber, and Hal Daume III. Feuding Families and Former Friends: Unsupervised Learning for Dynamic Fictional Relationships. North American Association for Computational Linguistics, 2016. Best paper award (2 out of 1592) (24% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_naacl_relationships.pdf Anupam Guha, Mohit Iyyer, Danny Bouman, and Jordan Boyd-Graber. Removing the Training Wheels: A Coreference Dataset that Entertains Humans and Challenges Computers. North American Association for Computational Linguistics, 2015. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_naacl_qb_coref.pdf Brianna Satinoff ------------------------- Yuening Hu, Jordan Boyd-Graber, Brianna Satinoff, and Alison Smith. Interactive Topic Modeling. Machine Learning, 2014. http://umiacs.umd.edu/~jbg/docs/2014_mlj_itm.pdf Jordan Boyd-Graber, Brianna Satinoff, He He, and Hal Daume III. Besting the Quiz Master: Crowdsourcing Incremental Classification Games. Empirical Methods in Natural Language Processing, 2012. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/qb_emnlp_2012.pdf Yuening Hu, Jordan Boyd-Graber, and Brianna Satinoff. Interactive Topic Modeling. Association for Computational Linguistics, 2011. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/itm.pdf Brianna Satinoff and Jordan Boyd-Graber. Trivial Classification: What features do humans use for classification?. Workshop on Crowdsourcing Technologies for Language and Cognition Studies, 2011. Christiane Fellbaum ------------------------- Sonya S. Nikolova, Jordan Boyd-Graber, and Christiane Fellbaum. Collecting Semantic Similarity Ratings to Connect Concepts in Assistive Communication Tools. Modeling, Learning and Processing of Text Technological Data Structures, 2011. http://umiacs.umd.edu/~jbg/docs/2011_book_chapter_evocation.pdf Sonya S. Nikolova, Jordan Boyd-Graber, Christiane Fellbaum, and Perry Cook. Better Vocabularies for Assistive Communication Aids: Connecting Terms using Semantic Networks and Untrained Annotators. ACM Conference on Computers and Accessibility, 2009. (31% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/evocation-viva.pdf Jordan Boyd-Graber, Christiane Fellbaum, Daniel Osherson, and Robert Schapire. Adding Dense, Weighted, Connections to WordNet. Proceedings of the Global WordNet Conference, 2006. http://umiacs.umd.edu/~jbg/docs/jbg-jeju.pdf Clay Templeton ------------------------- Clay Templeton, Travis Brown, Sayan Battacharyya, and Jordan Boyd-Graber. Mining the Dispatch under Supervision: Using Casualty Counts to Guide Topics from the Richmond Daily Dispatch Corpus. Chicago Colloquium on Digital Humanities and Computer Science, 2011. http://umiacs.umd.edu/~jbg/docs/slda_civil_war.pdf Clay Templeton, Kenneth R. Fleischmann, and Jordan Boyd-Graber. Simulating Audiences: Automating Analysis of Values, Attitudes, and Sentiment. IEEE International Conference on Social Computing, 2011. (10% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/simulating_audiences.pdf Clay Templeton, Kenneth R. Fleischmann, and Jordan Boyd-Graber. Comparing Values and Sentiment Using Mechanical Turk. iConference, 2011. http://umiacs.umd.edu/~jbg/docs/iconference-2011-comparing.pdf Kenneth R. Fleischmann, Clay Templeton, and Jordan Boyd-Graber. Modeling Diverse Standpoints in Text Classification: Learning to Be Human by Modeling Human Values. iConference, 2011. http://umiacs.umd.edu/~jbg/docs/iconference-2011-learning.pdf David M. Blei ------------------------- Aaron Gerow, Yuening Hu, Jordan Boyd-Graber, David M. Blei, and James A. Evans. Measuring Discursive Influence Across Scholarship. Proceedings of the National Academies of Science, 2018. Jonathan Chang, Jordan Boyd-Graber, and David M. Blei. Connections between the Lines: Augmenting Social Networks with Text. Knowledge Discovery and Data Mining, 2009. (9% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/kdd2009.pdf Jonathan Chang, Jordan Boyd-Graber, Chong Wang, Sean Gerrish, and David M. Blei. Reading Tea Leaves: How Humans Interpret Topic Models. Neural Information Processing Systems, 2009. Jonathan Chang and I shared a NIPS student award honorable mention for this paper (5 out of 1105) (24% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/nips2009-rtl.pdf Jordan Boyd-Graber and David M. Blei. Multilingual Topic Models for Unaligned Text. Uncertainty in Artificial Intelligence, 2009. For coverage of current state-of-the-art in cross-lingual topic models see: Yuening Hu, Ke Zhai, Vlad Eidelman, and Jordan Boyd-Graber. Polylingual Tree-Based Topic Models for Translation Domain Adaptation. Association for Computational Linguistics, 2014. (31% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/uai2009.pdf Jonathan Chang, Jordan Boyd-Graber, and David M. Blei. Discovering social networks from free text. 3rd Annual Machine Learning Symposium, 2008. Jordan Boyd-Graber and David M. Blei. Multilingual Topic Models. NIPS Workshop on Unsupervised Latent Variable Models, 2008. Jordan Boyd-Graber and David M. Blei. Syntactic Topic Models. Neural Information Processing Systems, 2008. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/nips2008.pdf Jordan Boyd-Graber and David M. Blei. PUTOP: Turning Predominant Senses into a Topic Model for WSD. 4th International Workshop on Semantic Evaluations, 2007. http://umiacs.umd.edu/~jbg/docs/jbg-SEMEVAL07.pdf Jordan Boyd-Graber, David M. Blei, and Xiaojin Zhu. A Topic Model for Word Sense Disambiguation. Empirical Methods in Natural Language Processing, 2007. (27% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/jbg-EMNLP07.pdf David Mimno ------------------------- Jordan Boyd-Graber, Yuening Hu, and David Mimno. Applications of Topic Models. 2017. http://umiacs.umd.edu/~jbg/http://www.nowpublishers.com/article/Details/INR-030 Jordan Boyd-Graber, David Mimno, and David Newman. Care and Feeding of Topic Models: Problems, Diagnostics, and Improvements. Handbook of Mixed Membership Models and Their Applications, 2014. http://umiacs.umd.edu/~jbg/docs/2014_book_chapter_care_and_feeding.pdf Eric Ringger ------------------------- Paul Felt, Eric Ringger, Kevin Seppi, and Jordan Boyd-Graber. Learning from Measurements in Crowdsourcing Models: Inferring Ground Truth from Diverse Annotation Types. International Conference on Computational Linguistics, 2018. (37% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2018_coling_measurements.pdf Paul Felt, Eric Ringger, Jordan Boyd-Graber, and Kevin Seppi. Making the Most of Crowdsourced Document Annotations: Confused Supervised LDA. Conference on Computational Natural Language Learning, 2015. This paper received the best paper award at CoNLL (30% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_conll_cslda.pdf Thang Nguyen, Jordan Boyd-Graber, Jeff Lund, Kevin Seppi, and Eric Ringger. Is your anchor going up or down? Fast and accurate supervised topic models. North American Association for Computational Linguistics, 2015. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_naacl_supervised_anchor.pdf Eric Wallace ------------------------- Eric Wallace and Jordan Boyd-Graber. Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions. ACL Student Research Workshop, 2018. http://umiacs.umd.edu/~jbg/http://aclweb.org/anthology/P18-3018 Shi Feng, Eric Wallace, Alvin Grissom II, Pedro Rodriguez, Mohit Iyyer, and Jordan Boyd-Graber. Pathologies of Neural Models Make Interpretation Difficult. Empirical Methods in Natural Language Processing, 2018. http://umiacs.umd.edu/~jbg/docs/2018_emnlp_rs.pdf Forough Poursabzi-Sangdeh ------------------------- Alison Smith, Tak Yeon Lee, Forough Poursabzi-Sangdeh, Jordan Boyd-Graber, Kevin Seppi, Niklas Elmqvist, and Leah Findlater. Evaluating Visual Representations for Topic Understanding and Their Effects on Manually Generated Labels. Transactions of the Association for Computational Linguistics, 2017. http://umiacs.umd.edu/~jbg/docs/2017_tacl_eval_tm_viz.pdf Forough Poursabzi-Sangdeh, Jordan Boyd-Graber, Leah Findlater, and Kevin Seppi. ALTO: Active Learning with Topic Overviews for Speeding Label Induction and Document Labeling. Association for Computational Linguistics, 2016. (28% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_acl_doclabel.pdf Alison Smith, Tak Yeon Lee, Forough Poursabzi-Sangdeh, Jordan Boyd-Graber, Kevin Seppi, Niklas Elmqvist, and Leah Findlater. Human-Centered and Interactive: Expanding the Impact of Topic Models. CHI Human Centred Machine Learning Workshop, 2016. Forough Poursabzi-Sangdeh and Jordan Boyd-Graber. Speeding Document Annotation with Topic Models. NAACL Student Research Workshop, 2015. Jason Chuang, John D. Wilkerson, Rebecca Weiss, Dustin Tingley, Brandon M. Stewart, Margaret E. Roberts, Forough Poursabzi-Sangdeh, Justin Grimmer, Leah Findlater, Jordan Boyd-Graber, and Jeffrey Heer. Computer-Assisted Content Analysis: Topic Models for Exploring Multiple Subjective Interpretations. NIPS Workshop on Human-Propelled Machine Learning, 2014. Hal Daume III ------------------------- Mohit Iyyer, Varun Manjunatha, Anupam Guha, Yogarshi Vyas, Jordan Boyd-Graber, Hal Daume III, and Larry Davis. The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narratives. Computer Vision and Pattern Recognition, 2017. http://umiacs.umd.edu/~jbg/docs/2017_cvpr_comics.pdf Khanh Nguyen, Jordan Boyd-Graber, and Hal Daume III. Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback. Empirical Methods in Natural Language Processing, 2017. http://umiacs.umd.edu/~jbg/docs/2017_emnlp_bandit_mt.pdf Hadi Amiri, Philip Resnik, Jordan Boyd-Graber, and Hal Daume III. Learning Text Pair Similarity with Context-sensitive Autoencoders. Association for Computational Linguistics, 2016. (28% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_acl_context_ae.pdf He He, Jordan Boyd-Graber, Kevin Kwok, and Hal Daume III. Opponent Modeling in Deep Reinforcement Learning. International Conference on Machine Learning, 2016. (24% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_icml_opponent.pdf Mohit Iyyer, Anupam Guha, Snigdha Chaturvedi, Jordan Boyd-Graber, and Hal Daume III. Feuding Families and Former Friends: Unsupervised Learning for Dynamic Fictional Relationships. North American Association for Computational Linguistics, 2016. Best paper award (2 out of 1592) (24% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_naacl_relationships.pdf He He, Jordan Boyd-Graber, and Hal Daume III. Interpretese vs. Translationese: The Uniqueness of Human Strategies in Simultaneous Interpretation. North American Association for Computational Linguistics, 2016. (29% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_naacl_interpretese.pdf Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, and Hal Daume III. Deep Unordered Composition Rivals Syntactic Methods for Text Classification. Association for Computational Linguistics, 2015. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_acl_dan.pdf He He, Alvin Grissom II, Jordan Boyd-Graber, and Hal Daume III. Syntax-based Rewriting for Simultaneous Machine Translation. Empirical Methods in Natural Language Processing, 2015. (24% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_emnlp_rewrite.pdf Jordan Boyd-Graber, Mohit Iyyer, He He, and Hal Daume III. Interactive Incremental Question Answering. Neural Information Processing Systems, 2015.This won the best demonstration award at NIPS 2015 Mohit Iyyer, Jordan Boyd-Graber, Leonardo Claudino, Richard Socher, and Hal Daume III. A Neural Network for Factoid Question Answering over Paragraphs. Empirical Methods in Natural Language Processing, 2014. The partial derivatives of "C" and "J" with respect to the parameters should be switched in Equation 7. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_emnlp_qb_rnn.pdf Alvin Grissom II, He He, Jordan Boyd-Graber, John Morgan, and Hal Daume III. Don't Until the Final Verb Wait: Reinforcement Learning for Simultaneous Machine Translation. Empirical Methods in Natural Language Processing, 2014. (30% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_emnlp_simtrans.pdf Mohit Iyyer, Jordan Boyd-Graber, and Hal Daume III. Generating Sentences from Semantic Vector Space Representations. NIPS Workshop on Learning Semantics, 2014. Yuening Hu, Jordan Boyd-Graber, Hal Daume III, and Z. Irene Ying. Binary to Bushy: Bayesian Hierarchical Clustering with the Beta Coalescent. Neural Information Processing Systems, 2013. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2013_coalescent.pdf Jordan Boyd-Graber, Brianna Satinoff, He He, and Hal Daume III. Besting the Quiz Master: Crowdsourcing Incremental Classification Games. Empirical Methods in Natural Language Processing, 2012. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/qb_emnlp_2012.pdf He He ------------------------- He He, Jordan Boyd-Graber, Kevin Kwok, and Hal Daume III. Opponent Modeling in Deep Reinforcement Learning. International Conference on Machine Learning, 2016. (24% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_icml_opponent.pdf He He, Jordan Boyd-Graber, and Hal Daume III. Interpretese vs. Translationese: The Uniqueness of Human Strategies in Simultaneous Interpretation. North American Association for Computational Linguistics, 2016. (29% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_naacl_interpretese.pdf He He, Alvin Grissom II, Jordan Boyd-Graber, and Hal Daume III. Syntax-based Rewriting for Simultaneous Machine Translation. Empirical Methods in Natural Language Processing, 2015. (24% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_emnlp_rewrite.pdf Jordan Boyd-Graber, Mohit Iyyer, He He, and Hal Daume III. Interactive Incremental Question Answering. Neural Information Processing Systems, 2015.This won the best demonstration award at NIPS 2015 Alvin Grissom II, He He, Jordan Boyd-Graber, John Morgan, and Hal Daume III. Don't Until the Final Verb Wait: Reinforcement Learning for Simultaneous Machine Translation. Empirical Methods in Natural Language Processing, 2014. (30% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_emnlp_simtrans.pdf Jordan Boyd-Graber, Brianna Satinoff, He He, and Hal Daume III. Besting the Quiz Master: Crowdsourcing Incremental Classification Games. Empirical Methods in Natural Language Processing, 2012. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/qb_emnlp_2012.pdf Jason Chuang ------------------------- Alison Smith, Jason Chuang, Yuening Hu, Jordan Boyd-Graber, and Leah Findlater. Concurrent Visualization of Relationships between Words and Topics in Topic Models. ACL Workshop on Workshop on Interactive Language Learning, Visualization, and Interfaces, 2014. Jason Chuang, John D. Wilkerson, Rebecca Weiss, Dustin Tingley, Brandon M. Stewart, Margaret E. Roberts, Forough Poursabzi-Sangdeh, Justin Grimmer, Leah Findlater, Jordan Boyd-Graber, and Jeffrey Heer. Computer-Assisted Content Analysis: Topic Models for Exploring Multiple Subjective Interpretations. NIPS Workshop on Human-Propelled Machine Learning, 2014. Jeff Lund ------------------------- Jeff Lund, Connor Cook, Kevin Seppi, and Jordan Boyd-Graber. Tandem Anchoring: A Multiword Anchor Approach for Interactive Topic Modeling. Association for Computational Linguistics, 2017. (22% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2017_acl_multiword_anchors.pdf You Lu, Jeff Lund, and Jordan Boyd-Graber. Why ADAGRAD Fails for Online Topic Modeling. Empirical Methods in Natural Language Processing, 2017. http://umiacs.umd.edu/~jbg/docs/2017_emnlp_adagrad_olda.pdf Thang Nguyen, Jordan Boyd-Graber, Jeff Lund, Kevin Seppi, and Eric Ringger. Is your anchor going up or down? Fast and accurate supervised topic models. North American Association for Computational Linguistics, 2015. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_naacl_supervised_anchor.pdf Jonathan Chang ------------------------- Viet-An Nguyen, Jordan Boyd-Graber, Philip Resnik, and Jonathan Chang. Learning a Concept Hierarchy from Multi-labeled Documents. Neural Information Processing Systems, 2014. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_nips_l2h.pdf Viet-An Nguyen, Jordan Boyd-Graber, Jonathan Chang, and Philip Resnik. Tree-Based Label Dependency Topic Models. NIPS Workshop on Topic Models: Computation, Application, and Evaluation, 2013. Jonathan Chang, Jordan Boyd-Graber, and David M. Blei. Connections between the Lines: Augmenting Social Networks with Text. Knowledge Discovery and Data Mining, 2009. (9% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/kdd2009.pdf Jonathan Chang, Jordan Boyd-Graber, Chong Wang, Sean Gerrish, and David M. Blei. Reading Tea Leaves: How Humans Interpret Topic Models. Neural Information Processing Systems, 2009. Jonathan Chang and I shared a NIPS student award honorable mention for this paper (5 out of 1105) (24% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/nips2009-rtl.pdf Jonathan Chang, Jordan Boyd-Graber, and David M. Blei. Discovering social networks from free text. 3rd Annual Machine Learning Symposium, 2008. Ke Zhai ------------------------- Yuening Hu, Ke Zhai, Vlad Eidelman, and Jordan Boyd-Graber. Polylingual Tree-Based Topic Models for Translation Domain Adaptation. Association for Computational Linguistics, 2014. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_acl_ptlda_mt.pdf Ke Zhai, Jordan Boyd-Graber, and Shay B. Cohen. Hybrid Online Inference with Adaptor Grammars. NIPS Workshop on Advances in Variational Inference, 2014. Ke Zhai, Jordan Boyd-Graber, and Shay B. Cohen. Online Adaptor Grammars with Hybrid Inference. Transactions of the Association for Computational Linguistics, 2014. http://umiacs.umd.edu/~jbg/docs/2014_tacl_ag_vb_online.pdf Ke Zhai and Jordan Boyd-Graber. Online Topic Models with Infinite Vocabulary. International Conference on Machine Learning, 2013. (20% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2013_icml_infvoc.pdf Yuening Hu, Ke Zhai, Vlad Edelman, and Jordan Boyd-Graber. Topic Models for Translation Domain Adaptation. NIPS Workshop on Topic Models: Computation, Application, and Evaluation, 2013. Ke Zhai, Jordan Boyd-Graber, Nima Asadi, and Mohamad Alkhouja. Mr. LDA: A Flexible Large Scale Topic Modeling Package using Variational Inference in MapReduce. ACM International Conference on World Wide Web, 2012. (12% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/mrlda.pdf Yuening Hu, Ke Zhai, Sinead Williamson, and Jordan Boyd-Graber. Modeling Images using Transformed Indian Buffet Processes. International Conference of Machine Learning, 2012. (27% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/mtibp_icml_2012.pdf Ke Zhai and Jordan Boyd-Graber. Online Topic Model with Infinite Vocabulary. Mid-Atlantic Student Colloquium on Speech, Language, and Learning, 2012. Kenneth R. Fleischmann ------------------------- Clay Templeton, Kenneth R. Fleischmann, and Jordan Boyd-Graber. Simulating Audiences: Automating Analysis of Values, Attitudes, and Sentiment. IEEE International Conference on Social Computing, 2011. (10% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/simulating_audiences.pdf Clay Templeton, Kenneth R. Fleischmann, and Jordan Boyd-Graber. Comparing Values and Sentiment Using Mechanical Turk. iConference, 2011. http://umiacs.umd.edu/~jbg/docs/iconference-2011-comparing.pdf Kenneth R. Fleischmann, Clay Templeton, and Jordan Boyd-Graber. Modeling Diverse Standpoints in Text Classification: Learning to Be Human by Modeling Human Values. iConference, 2011. http://umiacs.umd.edu/~jbg/docs/iconference-2011-learning.pdf Kevin Seppi ------------------------- Alison Smith, Varun Kumar, Jordan Boyd-Graber, Kevin Seppi, and Leah Findlater. User-Centered Design and Evaluation of a Human-in-the-Loop Topic Modeling System. Intelligent User Interfaces, 2018.Alison won a best student paper honorable mention (3 out of 300) (23% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2018_iui_itm.pdf Paul Felt, Eric Ringger, Kevin Seppi, and Jordan Boyd-Graber. Learning from Measurements in Crowdsourcing Models: Inferring Ground Truth from Diverse Annotation Types. International Conference on Computational Linguistics, 2018. (37% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2018_coling_measurements.pdf Jeff Lund, Connor Cook, Kevin Seppi, and Jordan Boyd-Graber. Tandem Anchoring: A Multiword Anchor Approach for Interactive Topic Modeling. Association for Computational Linguistics, 2017. (22% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2017_acl_multiword_anchors.pdf Alison Smith, Varun Kumar, Jordan Boyd-Graber, Kevin Seppi, and Leah Findlater. Accounting for Input Uncertainty in Human-in-the-Loop Systems. CHI 2017 Designing for Uncertainty Workshop, 2017. http://umiacs.umd.edu/~jbg/http://visualization.ischool.uw.edu/hci_uncertainty/papers/Paper11.pdf Tak Yeon Lee, Alison Smith, Kevin Seppi, Niklas Elmqvist, Jordan Boyd-Graber, and Leah Findlater. The Human Touch: How Non-expert Users Perceive, Interpret, and Fix Topic Models. International Journal of Human-Computer Studies, 2017. http://umiacs.umd.edu/~jbg/docs/2017_ijhcs_human_touch.pdf Alison Smith, Tak Yeon Lee, Forough Poursabzi-Sangdeh, Jordan Boyd-Graber, Kevin Seppi, Niklas Elmqvist, and Leah Findlater. Evaluating Visual Representations for Topic Understanding and Their Effects on Manually Generated Labels. Transactions of the Association for Computational Linguistics, 2017. http://umiacs.umd.edu/~jbg/docs/2017_tacl_eval_tm_viz.pdf Forough Poursabzi-Sangdeh, Jordan Boyd-Graber, Leah Findlater, and Kevin Seppi. ALTO: Active Learning with Topic Overviews for Speeding Label Induction and Document Labeling. Association for Computational Linguistics, 2016. (28% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_acl_doclabel.pdf Alison Smith, Tak Yeon Lee, Forough Poursabzi-Sangdeh, Jordan Boyd-Graber, Kevin Seppi, Niklas Elmqvist, and Leah Findlater. Human-Centered and Interactive: Expanding the Impact of Topic Models. CHI Human Centred Machine Learning Workshop, 2016. Paul Felt, Eric Ringger, Jordan Boyd-Graber, and Kevin Seppi. Making the Most of Crowdsourced Document Annotations: Confused Supervised LDA. Conference on Computational Natural Language Learning, 2015. This paper received the best paper award at CoNLL (30% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_conll_cslda.pdf Thang Nguyen, Jordan Boyd-Graber, Jeff Lund, Kevin Seppi, and Eric Ringger. Is your anchor going up or down? Fast and accurate supervised topic models. North American Association for Computational Linguistics, 2015. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_naacl_supervised_anchor.pdf Kimberly Glasgow ------------------------- Kimberly Glasgow, Clay Fink, and Jordan Boyd-Graber. Our grief is unspeakable: Measuring the community impact of a tragedy. The International AAAI Conference on Weblogs and Social Media, 2014. (20% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_icwsm_grief.pdf Jordan Boyd-Graber, Kimberly Glasgow, and Jackie Sauter Zajac. Spoiler Alert: Machine Learning Approaches to Detect Social Media Posts with Revelatory Information. ASIST 2013: The 76th Annual Meeting of the American Society for Information Science and Technology, 2013. http://umiacs.umd.edu/~jbg/docs/2013_spoiler.pdf Larry Davis ------------------------- Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, and Larry Davis. Learning to Color from Language. North American Association of Computational Linguistics, 2018. http://umiacs.umd.edu/~jbg/docs/2018_naacl_colorization.pdf Mohit Iyyer, Varun Manjunatha, Anupam Guha, Yogarshi Vyas, Jordan Boyd-Graber, Hal Daume III, and Larry Davis. The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narratives. Computer Vision and Pattern Recognition, 2017. http://umiacs.umd.edu/~jbg/docs/2017_cvpr_comics.pdf Leah Findlater ------------------------- Alison Smith, Varun Kumar, Jordan Boyd-Graber, Kevin Seppi, and Leah Findlater. User-Centered Design and Evaluation of a Human-in-the-Loop Topic Modeling System. Intelligent User Interfaces, 2018.Alison won a best student paper honorable mention (3 out of 300) (23% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2018_iui_itm.pdf Alison Smith, Varun Kumar, Jordan Boyd-Graber, Kevin Seppi, and Leah Findlater. Accounting for Input Uncertainty in Human-in-the-Loop Systems. CHI 2017 Designing for Uncertainty Workshop, 2017. http://umiacs.umd.edu/~jbg/http://visualization.ischool.uw.edu/hci_uncertainty/papers/Paper11.pdf Tak Yeon Lee, Alison Smith, Kevin Seppi, Niklas Elmqvist, Jordan Boyd-Graber, and Leah Findlater. The Human Touch: How Non-expert Users Perceive, Interpret, and Fix Topic Models. International Journal of Human-Computer Studies, 2017. http://umiacs.umd.edu/~jbg/docs/2017_ijhcs_human_touch.pdf Alison Smith, Tak Yeon Lee, Forough Poursabzi-Sangdeh, Jordan Boyd-Graber, Kevin Seppi, Niklas Elmqvist, and Leah Findlater. Evaluating Visual Representations for Topic Understanding and Their Effects on Manually Generated Labels. Transactions of the Association for Computational Linguistics, 2017. http://umiacs.umd.edu/~jbg/docs/2017_tacl_eval_tm_viz.pdf Forough Poursabzi-Sangdeh, Jordan Boyd-Graber, Leah Findlater, and Kevin Seppi. ALTO: Active Learning with Topic Overviews for Speeding Label Induction and Document Labeling. Association for Computational Linguistics, 2016. (28% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_acl_doclabel.pdf Alison Smith, Tak Yeon Lee, Forough Poursabzi-Sangdeh, Jordan Boyd-Graber, Kevin Seppi, Niklas Elmqvist, and Leah Findlater. Human-Centered and Interactive: Expanding the Impact of Topic Models. CHI Human Centred Machine Learning Workshop, 2016. Alison Smith, Jason Chuang, Yuening Hu, Jordan Boyd-Graber, and Leah Findlater. Concurrent Visualization of Relationships between Words and Topics in Topic Models. ACL Workshop on Workshop on Interactive Language Learning, Visualization, and Interfaces, 2014. Jason Chuang, John D. Wilkerson, Rebecca Weiss, Dustin Tingley, Brandon M. Stewart, Margaret E. Roberts, Forough Poursabzi-Sangdeh, Justin Grimmer, Leah Findlater, Jordan Boyd-Graber, and Jeffrey Heer. Computer-Assisted Content Analysis: Topic Models for Exploring Multiple Subjective Interpretations. NIPS Workshop on Human-Propelled Machine Learning, 2014. Leonardo Claudino ------------------------- Philip Resnik, William Armstrong, Leonardo Claudino, Thang Nguyen, Viet-An Nguyen, and Jordan Boyd-Graber. Beyond LDA: Exploring Supervised Topic Modeling for Depression-Related Language in Twitter. NAACL Workshop on Cognitive Modeling and Computational Linguistics, 2015. Mohit Iyyer, Jordan Boyd-Graber, Leonardo Claudino, Richard Socher, and Hal Daume III. A Neural Network for Factoid Question Answering over Paragraphs. Empirical Methods in Natural Language Processing, 2014. The partial derivatives of "C" and "J" with respect to the parameters should be switched in Equation 7. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_emnlp_qb_rnn.pdf Mohit Iyyer ------------------------- Shi Feng, Eric Wallace, Alvin Grissom II, Pedro Rodriguez, Mohit Iyyer, and Jordan Boyd-Graber. Pathologies of Neural Models Make Interpretation Difficult. Empirical Methods in Natural Language Processing, 2018. http://umiacs.umd.edu/~jbg/docs/2018_emnlp_rs.pdf Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, and Larry Davis. Learning to Color from Language. North American Association of Computational Linguistics, 2018. http://umiacs.umd.edu/~jbg/docs/2018_naacl_colorization.pdf Mohit Iyyer, Varun Manjunatha, Anupam Guha, Yogarshi Vyas, Jordan Boyd-Graber, Hal Daume III, and Larry Davis. The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narratives. Computer Vision and Pattern Recognition, 2017. http://umiacs.umd.edu/~jbg/docs/2017_cvpr_comics.pdf Anupam Guha, Mohit Iyyer, and Jordan Boyd-Graber. A Distorted Skull Lies in the Bottom Center: Identifying Paintings from Text Descriptions. NAACL Human-Computer Question Answering Workshop, 2016. http://umiacs.umd.edu/~jbg/docs/2016_naacl_paintings.pdf Mohit Iyyer, Anupam Guha, Snigdha Chaturvedi, Jordan Boyd-Graber, and Hal Daume III. Feuding Families and Former Friends: Unsupervised Learning for Dynamic Fictional Relationships. North American Association for Computational Linguistics, 2016. Best paper award (2 out of 1592) (24% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_naacl_relationships.pdf Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, and Hal Daume III. Deep Unordered Composition Rivals Syntactic Methods for Text Classification. Association for Computational Linguistics, 2015. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_acl_dan.pdf Jordan Boyd-Graber, Mohit Iyyer, He He, and Hal Daume III. Interactive Incremental Question Answering. Neural Information Processing Systems, 2015.This won the best demonstration award at NIPS 2015 Anupam Guha, Mohit Iyyer, Danny Bouman, and Jordan Boyd-Graber. Removing the Training Wheels: A Coreference Dataset that Entertains Humans and Challenges Computers. North American Association for Computational Linguistics, 2015. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_naacl_qb_coref.pdf Mohit Iyyer, Peter Enns, Jordan Boyd-Graber, and Philip Resnik. Political Ideology Detection Using Recursive Neural Networks. Association for Computational Linguistics, 2014. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_acl_rnn_ideology.pdf Mohit Iyyer, Jordan Boyd-Graber, Leonardo Claudino, Richard Socher, and Hal Daume III. A Neural Network for Factoid Question Answering over Paragraphs. Empirical Methods in Natural Language Processing, 2014. The partial derivatives of "C" and "J" with respect to the parameters should be switched in Equation 7. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_emnlp_qb_rnn.pdf Mohit Iyyer, Jordan Boyd-Graber, and Hal Daume III. Generating Sentences from Semantic Vector Space Representations. NIPS Workshop on Learning Semantics, 2014. Naho Orita ------------------------- Alvin Grissom II, Naho Orita, and Jordan Boyd-Graber. Incremental Prediction of Sentence-final Verbs. Conference on Computational Natural Language Learning, 2016. (20% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_conll_verbpred.pdf Naho Orita, Naomi Feldman, and Jordan Boyd-Graber. Quantifying the role of discourse topicality in speakers' choices of referring expressions. ACL Workshop on Cognitive Modeling and Computational Linguistics, 2014. Naho Orita, Rebecca McKeown, Naomi H. Feldman, Jeffrey Lidz, and Jordan Boyd-Graber. Discovering Pronoun Categories using Discourse Information. Proceedings of the Cognitive Science Society, 2013. http://umiacs.umd.edu/~jbg/docs/2013_cogsci_pronoun.pdf Niklas Elmqvist ------------------------- Tak Yeon Lee, Alison Smith, Kevin Seppi, Niklas Elmqvist, Jordan Boyd-Graber, and Leah Findlater. The Human Touch: How Non-expert Users Perceive, Interpret, and Fix Topic Models. International Journal of Human-Computer Studies, 2017. http://umiacs.umd.edu/~jbg/docs/2017_ijhcs_human_touch.pdf Alison Smith, Tak Yeon Lee, Forough Poursabzi-Sangdeh, Jordan Boyd-Graber, Kevin Seppi, Niklas Elmqvist, and Leah Findlater. Evaluating Visual Representations for Topic Understanding and Their Effects on Manually Generated Labels. Transactions of the Association for Computational Linguistics, 2017. http://umiacs.umd.edu/~jbg/docs/2017_tacl_eval_tm_viz.pdf Alison Smith, Tak Yeon Lee, Forough Poursabzi-Sangdeh, Jordan Boyd-Graber, Kevin Seppi, Niklas Elmqvist, and Leah Findlater. Human-Centered and Interactive: Expanding the Impact of Topic Models. CHI Human Centred Machine Learning Workshop, 2016. Paul Felt ------------------------- Paul Felt, Eric Ringger, Kevin Seppi, and Jordan Boyd-Graber. Learning from Measurements in Crowdsourcing Models: Inferring Ground Truth from Diverse Annotation Types. International Conference on Computational Linguistics, 2018. (37% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2018_coling_measurements.pdf Paul Felt, Eric Ringger, Jordan Boyd-Graber, and Kevin Seppi. Making the Most of Crowdsourced Document Annotations: Confused Supervised LDA. Conference on Computational Natural Language Learning, 2015. This paper received the best paper award at CoNLL (30% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_conll_cslda.pdf Pedro Rodriguez ------------------------- Shi Feng, Eric Wallace, Alvin Grissom II, Pedro Rodriguez, Mohit Iyyer, and Jordan Boyd-Graber. Pathologies of Neural Models Make Interpretation Difficult. Empirical Methods in Natural Language Processing, 2018. http://umiacs.umd.edu/~jbg/docs/2018_emnlp_rs.pdf Jordan Boyd-Graber, Shi Feng, and Pedro Rodriguez. Human-Computer Question Answering: The Case for Quizbowl. The NIPS '17 Competition: Building Intelligent Systems, 2018. http://umiacs.umd.edu/~jbg/docs/2018_nips_qbcomp.pdf Perry Cook ------------------------- Sonya S. Nikolova, Jordan Boyd-Graber, Christiane Fellbaum, and Perry Cook. Better Vocabularies for Assistive Communication Aids: Connecting Terms using Semantic Networks and Untrained Annotators. ACM Conference on Computers and Accessibility, 2009. (31% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/evocation-viva.pdf Xiaojuan Ma, Jordan Boyd-Graber, Sonya S. Nikolova, and Perry Cook. Speaking Through Pictures: Images vs. Icons. ACM Conference on Computers and Accessibility, 2009. (31% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/image_icon.pdf Sonya S. Nikolova, Jordan Boyd-Graber, and Perry Cook. The Design of ViVA: A Mixed-initiative Visual Vocabulary for Aphasia. Proceedings of the 27th international conference extended abstracts on Human factors in computing systems, 2009. http://umiacs.umd.edu/~jbg/docs/viva.pdf Philip Resnik ------------------------- Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. Adapting Topic Models using Lexical Associations with Tree Priors. Empirical Methods in Natural Language Processing, 2017. http://umiacs.umd.edu/~jbg/docs/2017_emnlp_tree_prior.pdf Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. A Discriminative Topic Model using Document Network Structure. Association for Computational Linguistics, 2016. (28% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_acl_docblock.pdf Hadi Amiri, Philip Resnik, Jordan Boyd-Graber, and Hal Daume III. Learning Text Pair Similarity with Context-sensitive Autoencoders. Association for Computational Linguistics, 2016. (28% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_acl_context_ae.pdf Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. Birds of a Feather in the Same Nest: A Discriminative Topic Model using Block-based Priors. Mid-Atlantic Student Colloquium on Speech, Language, and Learning, 2016. Viet-An Nguyen, Jordan Boyd-Graber, Philip Resnik, and Kristina Miler. Tea Party in the House: A Hierarchical Ideal Point Topic Model and Its Application to Republican Legislators in the 112th Congress. Association for Computational Linguistics, 2015. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_acl_teaparty.pdf Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. Birds of a Feather Linked Together: A Discriminative Topic Model using Link-based Priors. Empirical Methods in Natural Language Processing, 2015. (28% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_emnlp_hinge_link.pdf Philip Resnik, William Armstrong, Leonardo Claudino, Thang Nguyen, Viet-An Nguyen, and Jordan Boyd-Graber. Beyond LDA: Exploring Supervised Topic Modeling for Depression-Related Language in Twitter. NAACL Workshop on Cognitive Modeling and Computational Linguistics, 2015. Mohit Iyyer, Peter Enns, Jordan Boyd-Graber, and Philip Resnik. Political Ideology Detection Using Recursive Neural Networks. Association for Computational Linguistics, 2014. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_acl_rnn_ideology.pdf Viet-An Nguyen, Jordan Boyd-Graber, and Philip Resnik. Sometimes Average is Best: The Importance of Averaging for Prediction using MCMC Inference in Topic Modeling. Empirical Methods in Natural Language Processing, 2014. (30% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_emnlp_howto_gibbs.pdf Viet-An Nguyen, Jordan Boyd-Graber, Philip Resnik, Deborah Cai, Jennifer Midberry, and Yuanxin Wang. Modeling Topic Control to Detect Influence in Conversations using Nonparametric Topic Models. Machine Learning, 2014. http://umiacs.umd.edu/~jbg/docs/2014_mlj_influencer.pdf Viet-An Nguyen, Jordan Boyd-Graber, Philip Resnik, and Jonathan Chang. Learning a Concept Hierarchy from Multi-labeled Documents. Neural Information Processing Systems, 2014. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_nips_l2h.pdf Viet-An Nguyen, Jordan Boyd-Graber, Jonathan Chang, and Philip Resnik. Tree-Based Label Dependency Topic Models. NIPS Workshop on Topic Models: Computation, Application, and Evaluation, 2013. Viet-An Nguyen, Jordan Boyd-Graber, and Philip Resnik. Lexical and Hierarchical Topic Regression. Neural Information Processing Systems, 2013. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2013_shlda.pdf Viet-An Nguyen, Yuening Hu, Jordan Boyd-Graber, and Philip Resnik. Argviz: Interactive Visualization of Topic Dynamics in Multi-party Conversations. North American Association for Computational Linguistics, 2013. (50% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2013_argviz.pdf Vladimir Eidelman, Jordan Boyd-Graber, and Philip Resnik. Topic Models for Dynamic Translation Model Adaptation. Association for Computational Linguistics, 2012. For a more thorough evaluation and an exploration of more advanced topic models for machine translation, see: Yuening Hu, Ke Zhai, Vlad Eidelman, and Jordan Boyd-Graber. Polylingual Tree-Based Topic Models for Translation Domain Adaptation. Association for Computational Linguistics, 2014. (21% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/acl_2012_tm_for_mt.pdf Viet-An Nguyen, Jordan Boyd-Graber, and Philip Resnik. SITS: A Hierarchical Nonparametric Model using Speaker Identity for Topic Segmentation in Multiparty Conversations. Association for Computational Linguistics, 2012. (19% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/acl_2012_sits.pdf Viet-An Nguyen, Jordan Boyd-Graber, and Philip Resnik. "I Want to Talk About, Again, My Record On Energy …'': Modeling Topic Control in Conversations using Speaker-centric Nonparametric Topic Models. Mid-Atlantic Student Colloquium on Speech, Language, and Learning, 2012. Pranav Anand, Joseph King, Jordan Boyd-Graber, Earl Wagner, Craig Martell, Douglas W. Oard, and Philip Resnik. Believe Me: We Can Do This!. The AAAI 2011 workshop on Computational Models of Natural Argument, 2011. http://umiacs.umd.edu/~jbg/docs/persuasion.pdf Nitin Madnani, Jordan Boyd-Graber, and Philip Resnik. Measuring Transitivity Using Untrained Annotators. Creating Speech and Language Data With Amazon's Mechanical Turk, 2010. http://umiacs.umd.edu/~jbg/docs/madnani-boyd-graber-turk-workshop.pdf Jordan Boyd-Graber and Philip Resnik. Holistic Sentiment Analysis Across Languages: Multilingual Supervised Latent Dirichlet Allocation. Empirical Methods in Natural Language Processing, 2010. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/jbg-mlslda-2010.pdf Eric Hardisty, Jordan Boyd-Graber, and Philip Resnik. Modeling Perspective using Adaptor Grammars. Empirical Methods in Natural Language Processing, 2010. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/adapted_naive_bayes.pdf Shay B. Cohen ------------------------- Ke Zhai, Jordan Boyd-Graber, and Shay B. Cohen. Hybrid Online Inference with Adaptor Grammars. NIPS Workshop on Advances in Variational Inference, 2014. Ke Zhai, Jordan Boyd-Graber, and Shay B. Cohen. Online Adaptor Grammars with Hybrid Inference. Transactions of the Association for Computational Linguistics, 2014. http://umiacs.umd.edu/~jbg/docs/2014_tacl_ag_vb_online.pdf Shi Feng ------------------------- Shi Feng and Jordan Boyd-Graber. What AI can do for me: Evaluating Machine Learning Interpretations in Cooperative Play. Intelligent User Interfaces, 2019. http://umiacs.umd.edu/~jbg/docs/2019_iui_augment.pdf Shi Feng, Eric Wallace, Alvin Grissom II, Pedro Rodriguez, Mohit Iyyer, and Jordan Boyd-Graber. Pathologies of Neural Models Make Interpretation Difficult. Empirical Methods in Natural Language Processing, 2018. http://umiacs.umd.edu/~jbg/docs/2018_emnlp_rs.pdf Jordan Boyd-Graber, Shi Feng, and Pedro Rodriguez. Human-Computer Question Answering: The Case for Quizbowl. The NIPS '17 Competition: Building Intelligent Systems, 2018. http://umiacs.umd.edu/~jbg/docs/2018_nips_qbcomp.pdf Sonya S. Nikolova ------------------------- Sonya S. Nikolova, Jordan Boyd-Graber, and Christiane Fellbaum. Collecting Semantic Similarity Ratings to Connect Concepts in Assistive Communication Tools. Modeling, Learning and Processing of Text Technological Data Structures, 2011. http://umiacs.umd.edu/~jbg/docs/2011_book_chapter_evocation.pdf Sonya S. Nikolova, Jordan Boyd-Graber, Christiane Fellbaum, and Perry Cook. Better Vocabularies for Assistive Communication Aids: Connecting Terms using Semantic Networks and Untrained Annotators. ACM Conference on Computers and Accessibility, 2009. (31% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/evocation-viva.pdf Xiaojuan Ma, Jordan Boyd-Graber, Sonya S. Nikolova, and Perry Cook. Speaking Through Pictures: Images vs. Icons. ACM Conference on Computers and Accessibility, 2009. (31% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/image_icon.pdf Sonya S. Nikolova, Jordan Boyd-Graber, and Perry Cook. The Design of ViVA: A Mixed-initiative Visual Vocabulary for Aphasia. Proceedings of the 27th international conference extended abstracts on Human factors in computing systems, 2009. http://umiacs.umd.edu/~jbg/docs/viva.pdf Jordan Boyd-Graber, Sonya S. Nikolova, Karyn A. Moffatt, Kenrick C. Kin, Joshua Y. Lee, Lester W. Mackey, Marilyn M. Tremaine, and Maria M. Klawe. Participatory design with proxies: Developing a desktop-PDA system to support people with aphasia. Computer-Human Interaction, 2006. (23% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/paper673-boyd-graber.pdf Tak Yeon Lee ------------------------- Tak Yeon Lee, Alison Smith, Kevin Seppi, Niklas Elmqvist, Jordan Boyd-Graber, and Leah Findlater. The Human Touch: How Non-expert Users Perceive, Interpret, and Fix Topic Models. International Journal of Human-Computer Studies, 2017. http://umiacs.umd.edu/~jbg/docs/2017_ijhcs_human_touch.pdf Alison Smith, Tak Yeon Lee, Forough Poursabzi-Sangdeh, Jordan Boyd-Graber, Kevin Seppi, Niklas Elmqvist, and Leah Findlater. Evaluating Visual Representations for Topic Understanding and Their Effects on Manually Generated Labels. Transactions of the Association for Computational Linguistics, 2017. http://umiacs.umd.edu/~jbg/docs/2017_tacl_eval_tm_viz.pdf Alison Smith, Tak Yeon Lee, Forough Poursabzi-Sangdeh, Jordan Boyd-Graber, Kevin Seppi, Niklas Elmqvist, and Leah Findlater. Human-Centered and Interactive: Expanding the Impact of Topic Models. CHI Human Centred Machine Learning Workshop, 2016. Thang Nguyen ------------------------- Philip Resnik, William Armstrong, Leonardo Claudino, Thang Nguyen, Viet-An Nguyen, and Jordan Boyd-Graber. Beyond LDA: Exploring Supervised Topic Modeling for Depression-Related Language in Twitter. NAACL Workshop on Cognitive Modeling and Computational Linguistics, 2015. Thang Nguyen, Jordan Boyd-Graber, Jeff Lund, Kevin Seppi, and Eric Ringger. Is your anchor going up or down? Fast and accurate supervised topic models. North American Association for Computational Linguistics, 2015. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_naacl_supervised_anchor.pdf Thang Nguyen, Yuening Hu, and Jordan Boyd-Graber. Anchors Regularized: Adding Robustness and Extensibility to Scalable Topic-Modeling Algorithms. Association for Computational Linguistics, 2014. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_acl_anchor_reg.pdf Thang Nguyen, Yuening Hu, and Jordan Boyd-Graber. Evaluating Regularized Anchor Words. NIPS Workshop on Topic Models: Computation, Application, and Evaluation, 2013. Varun Kumar ------------------------- Alison Smith, Varun Kumar, Jordan Boyd-Graber, Kevin Seppi, and Leah Findlater. User-Centered Design and Evaluation of a Human-in-the-Loop Topic Modeling System. Intelligent User Interfaces, 2018.Alison won a best student paper honorable mention (3 out of 300) (23% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2018_iui_itm.pdf Alison Smith, Varun Kumar, Jordan Boyd-Graber, Kevin Seppi, and Leah Findlater. Accounting for Input Uncertainty in Human-in-the-Loop Systems. CHI 2017 Designing for Uncertainty Workshop, 2017. http://umiacs.umd.edu/~jbg/http://visualization.ischool.uw.edu/hci_uncertainty/papers/Paper11.pdf Varun Manjunatha ------------------------- Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, and Larry Davis. Learning to Color from Language. North American Association of Computational Linguistics, 2018. http://umiacs.umd.edu/~jbg/docs/2018_naacl_colorization.pdf Mohit Iyyer, Varun Manjunatha, Anupam Guha, Yogarshi Vyas, Jordan Boyd-Graber, Hal Daume III, and Larry Davis. The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narratives. Computer Vision and Pattern Recognition, 2017. http://umiacs.umd.edu/~jbg/docs/2017_cvpr_comics.pdf Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, and Hal Daume III. Deep Unordered Composition Rivals Syntactic Methods for Text Classification. Association for Computational Linguistics, 2015. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_acl_dan.pdf Viet-An Nguyen ------------------------- Viet-An Nguyen, Jordan Boyd-Graber, Philip Resnik, and Kristina Miler. Tea Party in the House: A Hierarchical Ideal Point Topic Model and Its Application to Republican Legislators in the 112th Congress. Association for Computational Linguistics, 2015. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_acl_teaparty.pdf Philip Resnik, William Armstrong, Leonardo Claudino, Thang Nguyen, Viet-An Nguyen, and Jordan Boyd-Graber. Beyond LDA: Exploring Supervised Topic Modeling for Depression-Related Language in Twitter. NAACL Workshop on Cognitive Modeling and Computational Linguistics, 2015. Viet-An Nguyen, Jordan Boyd-Graber, and Philip Resnik. Sometimes Average is Best: The Importance of Averaging for Prediction using MCMC Inference in Topic Modeling. Empirical Methods in Natural Language Processing, 2014. (30% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_emnlp_howto_gibbs.pdf Viet-An Nguyen, Jordan Boyd-Graber, Philip Resnik, Deborah Cai, Jennifer Midberry, and Yuanxin Wang. Modeling Topic Control to Detect Influence in Conversations using Nonparametric Topic Models. Machine Learning, 2014. http://umiacs.umd.edu/~jbg/docs/2014_mlj_influencer.pdf Viet-An Nguyen, Jordan Boyd-Graber, Philip Resnik, and Jonathan Chang. Learning a Concept Hierarchy from Multi-labeled Documents. Neural Information Processing Systems, 2014. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_nips_l2h.pdf Viet-An Nguyen, Jordan Boyd-Graber, and Stephen Altschul. Dirichlet Mixtures, the Dirichlet Process, and the Structure of Protein Space. Journal of Computational Biology, 2013. http://umiacs.umd.edu/~jbg/docs/2013_dp_protein.pdf Viet-An Nguyen, Jordan Boyd-Graber, Jonathan Chang, and Philip Resnik. Tree-Based Label Dependency Topic Models. NIPS Workshop on Topic Models: Computation, Application, and Evaluation, 2013. Viet-An Nguyen, Jordan Boyd-Graber, and Philip Resnik. Lexical and Hierarchical Topic Regression. Neural Information Processing Systems, 2013. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2013_shlda.pdf Viet-An Nguyen, Yuening Hu, Jordan Boyd-Graber, and Philip Resnik. Argviz: Interactive Visualization of Topic Dynamics in Multi-party Conversations. North American Association for Computational Linguistics, 2013. (50% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2013_argviz.pdf Viet-An Nguyen, Jordan Boyd-Graber, and Philip Resnik. SITS: A Hierarchical Nonparametric Model using Speaker Identity for Topic Segmentation in Multiparty Conversations. Association for Computational Linguistics, 2012. (19% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/acl_2012_sits.pdf Viet-An Nguyen, Jordan Boyd-Graber, and Philip Resnik. "I Want to Talk About, Again, My Record On Energy …'': Modeling Topic Control in Conversations using Speaker-centric Nonparametric Topic Models. Mid-Atlantic Student Colloquium on Speech, Language, and Learning, 2012. Weiwei Yang ------------------------- Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. Adapting Topic Models using Lexical Associations with Tree Priors. Empirical Methods in Natural Language Processing, 2017. http://umiacs.umd.edu/~jbg/docs/2017_emnlp_tree_prior.pdf Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. A Discriminative Topic Model using Document Network Structure. Association for Computational Linguistics, 2016. (28% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2016_acl_docblock.pdf Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. Birds of a Feather in the Same Nest: A Discriminative Topic Model using Block-based Priors. Mid-Atlantic Student Colloquium on Speech, Language, and Learning, 2016. Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. Birds of a Feather Linked Together: A Discriminative Topic Model using Link-based Priors. Empirical Methods in Natural Language Processing, 2015. (28% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2015_emnlp_hinge_link.pdf Yuening Hu ------------------------- Aaron Gerow, Yuening Hu, Jordan Boyd-Graber, David M. Blei, and James A. Evans. Measuring Discursive Influence Across Scholarship. Proceedings of the National Academies of Science, 2018. Jordan Boyd-Graber, Yuening Hu, and David Mimno. Applications of Topic Models. 2017. http://umiacs.umd.edu/~jbg/http://www.nowpublishers.com/article/Details/INR-030 Alison Smith, Jason Chuang, Yuening Hu, Jordan Boyd-Graber, and Leah Findlater. Concurrent Visualization of Relationships between Words and Topics in Topic Models. ACL Workshop on Workshop on Interactive Language Learning, Visualization, and Interfaces, 2014. Thang Nguyen, Yuening Hu, and Jordan Boyd-Graber. Anchors Regularized: Adding Robustness and Extensibility to Scalable Topic-Modeling Algorithms. Association for Computational Linguistics, 2014. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_acl_anchor_reg.pdf Yuening Hu, Ke Zhai, Vlad Eidelman, and Jordan Boyd-Graber. Polylingual Tree-Based Topic Models for Translation Domain Adaptation. Association for Computational Linguistics, 2014. (26% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2014_acl_ptlda_mt.pdf Yuening Hu, Jordan Boyd-Graber, Brianna Satinoff, and Alison Smith. Interactive Topic Modeling. Machine Learning, 2014. http://umiacs.umd.edu/~jbg/docs/2014_mlj_itm.pdf Thang Nguyen, Yuening Hu, and Jordan Boyd-Graber. Evaluating Regularized Anchor Words. NIPS Workshop on Topic Models: Computation, Application, and Evaluation, 2013. Yuening Hu, Ke Zhai, Vlad Edelman, and Jordan Boyd-Graber. Topic Models for Translation Domain Adaptation. NIPS Workshop on Topic Models: Computation, Application, and Evaluation, 2013. Yuening Hu, Jordan Boyd-Graber, Hal Daume III, and Z. Irene Ying. Binary to Bushy: Bayesian Hierarchical Clustering with the Beta Coalescent. Neural Information Processing Systems, 2013. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2013_coalescent.pdf Viet-An Nguyen, Yuening Hu, Jordan Boyd-Graber, and Philip Resnik. Argviz: Interactive Visualization of Topic Dynamics in Multi-party Conversations. North American Association for Computational Linguistics, 2013. (50% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/2013_argviz.pdf Yuening Hu and Jordan Boyd-Graber. Efficient Tree-Based Topic Modeling. Association for Computational Linguistics, 2012. (21% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/acl_2012_fttm.pdf Yuening Hu and Jordan Boyd-Graber. Suggesting Constraints for Interactive Topic Modeling. ICML Workshop on Machine Learning in Human Computation and Crowdsourcing, 2012. Yuening Hu, Ke Zhai, Sinead Williamson, and Jordan Boyd-Graber. Modeling Images using Transformed Indian Buffet Processes. International Conference of Machine Learning, 2012. (27% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/mtibp_icml_2012.pdf Yuening Hu and Jordan Boyd-Graber. Bayesian Hierarchical Clustering with Beta Coalescents. Mid-Atlantic Student Colloquium on Speech, Language, and Learning, 2012. Yuening Hu, Jordan Boyd-Graber, and Brianna Satinoff. Interactive Topic Modeling. Association for Computational Linguistics, 2011. (25% Acceptance Rate) http://umiacs.umd.edu/~jbg/docs/itm.pdf