Pubs (Year)

Sort by: Year Category Authors Venue Project
  • Jordan Boyd-Graber and Benjamin Börschinger. What Question Answering can Learn from Trivia Nerds. ArXiv, Preprint. [Bibtex]
  • Pedro Rodriguez, Shi Feng, Mohit Iyyer, He He, and Jordan Boyd-Graber. Quizbowl: The Case for Incremental Question Answering. ArXiv, Preprint. [Webpage] [Bibtex]
  • Benjamin Börschinger, Jordan Boyd-Graber, Christian Buck, Jannis Bulian, Massimiliano Ciaramita, Michelle Chen Huebscher, Wojciech Gajewski, Yannic Kilcher, Rodrigo Nogueira, and Lierni Sestorain Saralegu. Meta Answering for Machine Reading. ArXiv, Preprint. [Bibtex]
  • Mozhi Zhang, Yoshinari Fujinuma, and Jordan Boyd-Graber. Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification. Association for the Advancement of Artificial Intelligence, 2020. [Bibtex]
  • Eric Wallace, Pedro Rodriguez, Shi Feng, Ikuya Yamada, and Jordan Boyd-Graber. Trick Me If You Can: Human-in-the-loop Generation of Adversarial Question Answering Examples. Transactions of the Association of Computational Linguistics, 2019. [Bibtex]
  • Dasha Pruss, Yoshinari Fujinuma, Ashlynn Daughton, Michael Paul, Brad Arnot, Danielle Szafir, and Jordan Boyd-Graber. Zika discourse in the Americas: A multilingual topic analysis of Twitter. PlosOne, 2019. [Data] [Bibtex]
  • Shi Feng and Jordan Boyd-Graber. What AI can do for me: Evaluating Machine Learning Interpretations in Cooperative Play. Intelligent User Interfaces, 2019. [Bibtex]
  • Ahmed Elgohary, Denis Peskov, and Jordan Boyd-Graber. Can You Unpack That? Learning to Rewrite Questions-in-Context. Empirical Methods in Natural Language Processing, 2019. [Data] [Bibtex]
  • Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. A Multilingual Topic Model for Learning Weighted Topic Links Across Incomparable Corpora. Empirical Methods in Natural Language Processing, 2019. [Bibtex]
  • Denis Peskov, Joe Barrow, Pedro Rodriguez, Graham Neubig, and Jordan Boyd-Graber. Mitigating Noisy Inputs for Question Answering. Conference of the International Speech Communication Association, 2019. [Bibtex]
  • Varun Kumar, Alison Smith, Leah Findlater, Kevin Seppi, and Jordan Boyd-Graber. Why Didn't You Listen to Me? Comparing User Control of Human-in-the-Loop Topic Models. Association for Computational Linguistics, 2019. [Bibtex]
  • Eric Wallace, Shi Feng, and Jordan Boyd-Graber. Misleading Failures of Partial-input Baselines. Association for Computational Linguistics, 2019. [Bibtex]
  • Jeffrey Lund, Piper Armstrong, Wilson Fearn, Stephen Cowley, Courtni Byun, Jordan Boyd-Graber, and Kevin Seppi. Automatic and Human Evaluation of Local Topic Quality. Association for Computational Linguistics, 2019. [Code] [Bibtex]
  • Mozhi Zhang and Keyulu Xu and Ken-ichi Kawarabayashi and Stefanie Jegelka and Jordan Boyd-Graber. Are Girls Neko or Shōjo? Aligning Word Embeddings by Enforcing Isomorphism. Association for Computational Linguistics, 2019. [Bibtex]
  • Yoshinari Fujinuma, Michael Paul, and Jordan Boyd-Graber. A Resource-Free Evaluation Metric for Cross-Lingual Word Embeddings Based on Graph Modularity. Association for Computational Linguistics, 2019. [Bibtex]
  • Jordan Boyd-Graber, Shi Feng, and Pedro Rodriguez. Human-Computer Question Answering: The Case for Quizbowl. The NIPS '17 Competition: Building Intelligent Systems, 2018. [Bibtex]
  • 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. [Journal] [Bibtex]
  • Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, and Larry Davis. Learning to Color from Language. North American Association of Computational Linguistics, 2018. [Bibtex]
  • Shudong Hao, Michael J. Paul, and Jordan Boyd-Graber. Lessons from the Bible on Modern Topics: Multilingual Topic Model Evaluation on Low-Resource Languages. North American Association for Computational Linguistics, 2018. [Bibtex]
  • Michelle Yuan, Benjamin Van Durme, and Jordan Boyd-Graber. Multilingual Anchoring: Interactive Topic Modeling and Alignment Across Languages. Neural Information Processing Systems, 2018. [Code] [Bibtex]
  • 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. [Bibtex]
  • 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. [Bibtex] Alison won a best student paper honorable mention (3 out of 300)
  • 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. [Blog Post] [Bibtex]
  • Ahmed Elgohary, Chen Zhao, and Jordan Boyd-Graber. Dataset and Baselines for Sequential Open-Domain Question Answering. Empirical Methods in Natural Language Processing, 2018. [Bibtex]
  • Shi Feng, Eric Wallace, and Jordan Boyd-Graber. Interpreting Neural Networks with Nearest Neighbors. EMNLP Workshop on BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, 2018. [Bibtex]
  • Craig Stewart, Nikolai Vogler, Junjie Hu, Jordan Boyd-Graber, and Graham Neubig. Automatic Estimation of Simultaneous Interpreter Performance. Association for Computational Linguistics, 2018. [Bibtex]
  • Mozhi Zhang, Yoshinari Fujinuma, and Jordan Boyd-Graber. Exploiting Cross-Lingual Subword Similarities in Low-Resource Document Classification. ACL Workshop on Deep Learning Approaches for Low-Resource Natural Language Processing, 2018. [Bibtex]
  • Eric Wallace and Jordan Boyd-Graber. Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions. ACL Student Research Workshop, 2018. [Bibtex]
  • 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. [Journal] [Data] [Bibtex]
  • Jordan Boyd-Graber. Humans and Computers Working Together to Measure Machine Learning Interpretability. The Bridge, 2017. [Journal] [Bibtex]
  • 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. [Journal] [Bibtex]
  • You Lu, Jeff Lund, and Jordan Boyd-Graber. Why ADAGRAD Fails for Online Topic Modeling. Empirical Methods in Natural Language Processing, 2017. [Bibtex]
  • Khanh Nguyen, Jordan Boyd-Graber, and Hal Daumé III. Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback. Empirical Methods in Natural Language Processing, 2017. [Code] [Bibtex]
  • Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. Adapting Topic Models using Lexical Associations with Tree Priors. Empirical Methods in Natural Language Processing, 2017. [Bibtex]
  • Mohit Iyyer, Varun Manjunatha, Anupam Guha, Yogarshi Vyas, Jordan Boyd-Graber, Hal Daumé III, and Larry Davis. The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narratives. Computer Vision and Pattern Recognition, 2017. [Code/Data] [Bibtex]
  • 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. [Bibtex]
  • 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. [Code] [Bibtex]
  • Jordan Boyd-Graber, Yuening Hu, and David Mimno. Applications of Topic Models. 2017. [Preprint] [Bibtex]
  • He He, Jordan Boyd-Graber, and Hal Daumé III. Interpretese vs. Translationese: The Uniqueness of Human Strategies in Simultaneous Interpretation. North American Association for Computational Linguistics, 2016. [Talk] [Bibtex]
  • Mohit Iyyer, Anupam Guha, Snigdha Chaturvedi, Jordan Boyd-Graber, and Hal Daumé III. Feuding Families and Former Friends: Unsupervised Learning for Dynamic Fictional Relationships. North American Association for Computational Linguistics, 2016. [Code/Data] [Bibtex] Best paper award (2 out of 1592)
  • Md Arafat Sultan, Jordan Boyd-Graber, and Tamara Sumner. Bayesian Supervised Domain Adaptation for Short Text Similarity. North American Association for Computational Linguistics, 2016. [Talk] [Bibtex]
  • 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. [Data] [Bibtex]
  • 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. [Bibtex]
  • He He, Jordan Boyd-Graber, Kevin Kwok, and Hal Daumé III. Opponent Modeling in Deep Reinforcement Learning. International Conference on Machine Learning, 2016. [Video] [Bibtex]
  • Alvin Grissom II, Naho Orita, and Jordan Boyd-Graber. Incremental Prediction of Sentence-final Verbs. Conference on Computational Natural Language Learning, 2016. [Bibtex]
  • 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. [Bibtex]
  • Evgeny Klochikhin and Jordan Boyd-Graber. Text Analysis. Big Data and Social Science Research: Theory and Practical Approaches, 2016. [Bibtex]
  • Hadi Amiri, Philip Resnik, Jordan Boyd-Graber, and Hal Daumé III. Learning Text Pair Similarity with Context-sensitive Autoencoders. Association for Computational Linguistics, 2016. [Code] [Bibtex]
  • 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. [Code] [Bibtex]
  • Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. A Discriminative Topic Model using Document Network Structure. Association for Computational Linguistics, 2016. [Supplement] [Bibtex]
  • 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. [Code/Data] [Slides] [Video] [LaTeX] [Bibtex]
  • 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. [Bibtex]
  • Jordan Boyd-Graber, Mohit Iyyer, He He, and Hal Daumé III. Interactive Incremental Question Answering. Neural Information Processing Systems, 2015. [Bibtex] This won the best demonstration award at NIPS 2015
  • 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. [Bibtex]
  • Forough Poursabzi-Sangdeh and Jordan Boyd-Graber. Speeding Document Annotation with Topic Models. NAACL Student Research Workshop, 2015. [Bibtex]
  • Stephen H. Bach, Bert Huang, Jordan Boyd-Graber, and Lise Getoor. Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs. International Conference on Machine Learning, 2015. [Video] [Bibtex]
  • He He, Alvin Grissom II, Jordan Boyd-Graber, and Hal Daumé III. Syntax-based Rewriting for Simultaneous Machine Translation. Empirical Methods in Natural Language Processing, 2015. [Bibtex]
  • Yi Yang, Doug Downey, and Jordan Boyd-Graber. Efficient Methods for Incorporating Knowledge into Topic Models. Empirical Methods in Natural Language Processing, 2015. [Code] [Bibtex]
  • 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. [Bibtex]
  • 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. [Talk] [Bibtex] This paper received the best paper award at CoNLL
  • 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. [Talk] [Code] [LaTeX] [Bibtex]
  • Vlad Niculae, Srijan Kumar, Jordan Boyd-Graber, and Cristian Danescu-Niculescu-Mizil. Linguistic Harbingers of Betrayal: A Case Study on an Online Strategy Game. Association for Computational Linguistics, 2015. [Code/Data] [Bibtex]
  • Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, and Hal Daumé III. Deep Unordered Composition Rivals Syntactic Methods for Text Classification. Association for Computational Linguistics, 2015. [Slides] [Code] [Talk] [Bibtex]
  • Ke Zhai, Jordan Boyd-Graber, and Shay B. Cohen. Online Adaptor Grammars with Hybrid Inference. Transactions of the Association for Computational Linguistics, 2014. [Code] [Bibtex]
  • 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. [Bibtex]
  • Viet-An Nguyen, Jordan Boyd-Graber, Philip Resnik, and Jonathan Chang. Learning a Concept Hierarchy from Multi-labeled Documents. Neural Information Processing Systems, 2014. [Code] [Bibtex]
  • Mohit Iyyer, Jordan Boyd-Graber, and Hal Daumé III. Generating Sentences from Semantic Vector Space Representations. NIPS Workshop on Learning Semantics, 2014. [Bibtex]
  • 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. [Bibtex]
  • Ke Zhai, Jordan Boyd-Graber, and Shay B. Cohen. Hybrid Online Inference with Adaptor Grammars. NIPS Workshop on Advances in Variational Inference, 2014. [Bibtex]
  • 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. [Journal] [Code] [Data] [Bibtex]
  • Yuening Hu, Jordan Boyd-Graber, Brianna Satinoff, and Alison Smith. Interactive Topic Modeling. Machine Learning, 2014. [Journal] [Frontend Code] [Backend Code] [Bibtex]
  • 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. [Bibtex]
  • 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. [Bibtex]
  • Alvin Grissom II, He He, Jordan Boyd-Graber, John Morgan, and Hal Daumé III. Don't Until the Final Verb Wait: Reinforcement Learning for Simultaneous Machine Translation. Empirical Methods in Natural Language Processing, 2014. [Talk] [Bibtex]
  • Mohit Iyyer, Jordan Boyd-Graber, Leonardo Claudino, Richard Socher, and Hal Daumé III. A Neural Network for Factoid Question Answering over Paragraphs. Empirical Methods in Natural Language Processing, 2014. [Code/Data] [Bibtex] The partial derivatives of "C" and "J" with respect to the parameters should be switched in Equation 7.
  • Yuening Hu, Ke Zhai, Vlad Eidelman, and Jordan Boyd-Graber. Polylingual Tree-Based Topic Models for Translation Domain Adaptation. Association for Computational Linguistics, 2014. [Code] [Bibtex]
  • Mohit Iyyer, Peter Enns, Jordan Boyd-Graber, and Philip Resnik. Political Ideology Detection Using Recursive Neural Networks. Association for Computational Linguistics, 2014. [Data] [Bibtex]
  • Thang Nguyen, Yuening Hu, and Jordan Boyd-Graber. Anchors Regularized: Adding Robustness and Extensibility to Scalable Topic-Modeling Algorithms. Association for Computational Linguistics, 2014. [Talk] [Bibtex]
  • 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. [Bibtex]
  • 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. [Bibtex]
  • 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. [Bibtex]
  • 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. [Bibtex]
  • Viet-An Nguyen, Jordan Boyd-Graber, and Philip Resnik. Lexical and Hierarchical Topic Regression. Neural Information Processing Systems, 2013. [Supplement] [Bibtex]
  • Yuening Hu, Jordan Boyd-Graber, Hal Daumé III, and Z. Irene Ying. Binary to Bushy: Bayesian Hierarchical Clustering with the Beta Coalescent. Neural Information Processing Systems, 2013. [Supplement] [Data] [Bibtex]
  • 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. [Bibtex]
  • 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. [Bibtex]
  • Thang Nguyen, Yuening Hu, and Jordan Boyd-Graber. Evaluating Regularized Anchor Words. NIPS Workshop on Topic Models: Computation, Application, and Evaluation, 2013. [Bibtex]
  • 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. [Journal] [Bibtex]
  • Ke Zhai and Jordan Boyd-Graber. Online Topic Models with Infinite Vocabulary. International Conference on Machine Learning, 2013. [Poster] [Talk] [Code] [Bibtex]
  • 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. [Data] [Bibtex]
  • Asad B. Sayeed, Jordan Boyd-Graber, Bryan Rusk, and Amy Weinberg. Grammatical structures for word-level sentiment detection. North American Association of Computational Linguistics, 2012. [Data] [Bibtex]
  • 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. [Bibtex]
  • Ke Zhai and Jordan Boyd-Graber. Online Topic Model with Infinite Vocabulary. Mid-Atlantic Student Colloquium on Speech, Language, and Learning, 2012. [Bibtex]
  • Yuening Hu and Jordan Boyd-Graber. Bayesian Hierarchical Clustering with Beta Coalescents. Mid-Atlantic Student Colloquium on Speech, Language, and Learning, 2012. [Bibtex]
  • Yuening Hu, Ke Zhai, Sinead Williamson, and Jordan Boyd-Graber. Modeling Images using Transformed Indian Buffet Processes. International Conference of Machine Learning, 2012. [Code] [Data] [Presentation] [Bibtex]
  • Yuening Hu and Jordan Boyd-Graber. Suggesting Constraints for Interactive Topic Modeling. ICML Workshop on Machine Learning in Human Computation and Crowdsourcing, 2012. [Bibtex]
  • Jordan Boyd-Graber, Brianna Satinoff, He He, and Hal Daumé III. Besting the Quiz Master: Crowdsourcing Incremental Classification Games. Empirical Methods in Natural Language Processing, 2012. [Presentation] [Data] [Bibtex]
  • 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. [Data] [Code] [Slides] [Appendix] [Bibtex]
  • Vladimir Eidelman, Jordan Boyd-Graber, and Philip Resnik. Topic Models for Dynamic Translation Model Adaptation. Association for Computational Linguistics, 2012. [Presentation] [More Recent Paper] [Bibtex] 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.
  • Yuening Hu and Jordan Boyd-Graber. Efficient Tree-Based Topic Modeling. Association for Computational Linguistics, 2012. [Bibtex]
  • 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. [Code] [Slides] [Bibtex]
  • 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. [Bibtex]
  • Clay Templeton, Kenneth R. Fleischmann, and Jordan Boyd-Graber. Comparing Values and Sentiment Using Mechanical Turk. iConference, 2011. [Bibtex]
  • 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. [Bibtex]
  • 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. [Data] [Presentation] [Bibtex]
  • Jordan Boyd-Graber. Linguistic Resource Creation in a Web 2.0 World. NSF Workshop on Collaborative Annotation, 2011. [Bibtex]
  • 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. [Ratings] [Bibtex]
  • 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. [Bibtex]
  • 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. [Bibtex]
  • Yuening Hu, Jordan Boyd-Graber, and Brianna Satinoff. Interactive Topic Modeling. Association for Computational Linguistics, 2011. [Slides] [Code] [Bibtex]
  • Eric Hardisty, Jordan Boyd-Graber, and Philip Resnik. Modeling Perspective using Adaptor Grammars. Empirical Methods in Natural Language Processing, 2010. [Bibtex]
  • Jordan Boyd-Graber and Philip Resnik. Holistic Sentiment Analysis Across Languages: Multilingual Supervised Latent Dirichlet Allocation. Empirical Methods in Natural Language Processing, 2010. [Data] [Bibtex]
  • Nitin Madnani, Jordan Boyd-Graber, and Philip Resnik. Measuring Transitivity Using Untrained Annotators. Creating Speech and Language Data With Amazon's Mechanical Turk, 2010. [Data] [Bibtex]
  • Jordan Boyd-Graber. Linguistic Extensions of Topic Models. Ph.D. thesis, Princeton University, 2010. [Bibtex]
  • Jordan Boyd-Graber and David M. Blei. Multilingual Topic Models for Unaligned Text. Uncertainty in Artificial Intelligence, 2009. [More Recent Paper] [Bibtex] 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.
  • 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. [Bibtex]
  • 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. [Data] [Presentation] [Video] [Bibtex] Jonathan Chang and I shared a NIPS student award honorable mention for this paper (5 out of 1105)
  • Jonathan Chang, Jordan Boyd-Graber, and David M. Blei. Connections between the Lines: Augmenting Social Networks with Text. Knowledge Discovery and Data Mining, 2009. [Code] [Slides] [Video] [Pie Fight] [Bibtex]
  • Xiaojuan Ma, Jordan Boyd-Graber, Sonya S. Nikolova, and Perry Cook. Speaking Through Pictures: Images vs. Icons. ACM Conference on Computers and Accessibility, 2009. [Slides] [Bibtex]
  • 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. [Ratings] [Slides] [Bibtex]
  • Jordan Boyd-Graber and David M. Blei. Syntactic Topic Models. Neural Information Processing Systems, 2008. [Presentation] [Extended Version] [Bibtex]
  • Jordan Boyd-Graber and David M. Blei. Multilingual Topic Models. NIPS Workshop on Unsupervised Latent Variable Models, 2008. [Bibtex]
  • Jonathan Chang, Jordan Boyd-Graber, and David M. Blei. Discovering social networks from free text. 3rd Annual Machine Learning Symposium, 2008. [Bibtex]
  • Jordan Boyd-Graber, David M. Blei, and Xiaojin Zhu. A Topic Model for Word Sense Disambiguation. Empirical Methods in Natural Language Processing, 2007. [Presentation] [Code] [Bibtex]
  • Jordan Boyd-Graber and David M. Blei. PUTOP: Turning Predominant Senses into a Topic Model for WSD. 4th International Workshop on Semantic Evaluations, 2007. [Bibtex]
  • Jordan Boyd-Graber, Christiane Fellbaum, Daniel Osherson, and Robert Schapire. Adding Dense, Weighted, Connections to WordNet. Proceedings of the Global WordNet Conference, 2006. [Presentation] [Data] [Bibtex]
  • 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. [Presentation] [Bibtex]
  • Alexander Geyken and Jordan Boyd-Graber. Automatic classification of multi-word expressions in print dictionaries. Linguisticae Investigationes, 2003. [Bibtex]
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