Brendan T. O'Connor Assistant Professor, College of Information and Computer Sciences University of Massachusetts Amherst Email: brenocon@cs.umass.edu    Twitter: @brendan642 Room 238, Computer Science Building, 140 Governors Drive, Amherst, MA 01003

I am an assistant professor in the College of Information and Computer Sciences at University of Massachusetts Amherst (since Fall 2014). I am affiliated with the Computational Social Science Institute, the Initiative in Cognitive Science, and the Centers for Data Science and Intelligent Information Retrieval.

Links: SLANG Lab, Teaching, CV, Bio, Talks, Notes, Misc.

Currently teaching: COMPSCI 690D: Deep Learning for Natural Language Processing.

Talks on current research:

• Identifying police killings from text with distant supervision
• Demographic bias in social media language analysis: a case study of African-American English

Press coverage:

• This AI reads the news to keep tabs on US police shootings, New Scientist, September 2017.
• AI Programs Are Learning to Exclude Some African-American Voices, MIT Technology Review, August 2017.

Other news: I was recently awarded an NSF CAREER award for Social Aggregate Measurement from Text (Feb 2019).

Research:
What can statistical text analysis tell us about society? I develop text analysis methods that can help answer social science questions. I'm interested in statistical machine learning and natural language processing, especially when informed by or applied to areas like political science or sociolinguistics. My work often uses text data from news and social media.

See also my earlier research statement or publications below.

There is a rich set of other faculty at UMass interested in areas from computational social science to natural language processing. See the Computational Social Science Institute (CSSI) website, and this list of computation+language researchers and courses.

Background:
I joined UMass after receiving my PhD from Carnegie Mellon University's Machine Learning Department. I have also been a Visiting Fellow at Harvard IQSS, and interned with the Facebook Data Science team. Before grad school, I worked on crowdsourced annotations at CrowdFlower / Dolores Labs, and natural language search at Powerset. I started studying the intersection of AI and social science as an undergrad and masters student in Stanford Symbolic Systems (cognitive science).

Publications

• Uncertainty-aware generative models for inferring document class prevalence.
  Katherine Keith and Brendan O'Connor.
  Proceedings of EMNLP 2018.
  • Website
• Twitter Universal Dependency Parsing for African-American and Mainstream American English.
  Su Lin Blodgett, Johnny Tian-Zheng Wei, and Brendan O'Connor.
  Proceedings of ACL 2018.
• Monte Carlo Syntax Marginals for Exploring and Using Dependency Parses.
  Katherine Keith, Su Lin Blodgett, and Brendan O'Connor
  Proceedings of NAACL 2018.
• Relational Summarization for Corpus Analysis.

  Abram Handler and Brendan O'Connor

  Proceedings of NAACL 2018.
• Understanding the Representational Power of Neural Retrieval Models Using NLP Tasks.
  Daniel Cohen, Brendan O'Connor, and W. Bruce Croft.
  Proceedings of ICTIR 2018.
• Evaluating Syntactic Properties of Seq2seq Output with a Broad Coverage HPSG: A Case Study on Machine Translation.
  Johnny Tian-Zheng Wei, Khiem Pham, Brian Dillon, and Brendan O'Connor,
  BlackboxNLP workshop at EMNLP 2018 (Analyzing and interpreting neural networks for NLP).
• A Probabilistic Approach for Learning with Label Proportions Applied to the US Presidential Election.
  Tao Sun, Daniel Sheldon, and Brendan O'Connor.
  Proceedings of ICDM 2017.
• Identifying civilians killed by police with distantly supervised entity-event extraction.
  Katherine Keith, Abram Handler, Michael Pinkham, Cara Magliozzi, Joshua McDuffie, and Brendan O'Connor.
  Proceedings of EMNLP 2017.
  • Website
• Rookie: A unique approach for exploring news archives.
  Abram Handler and Brendan O'Connor.
  Workshop on Data Science + Journalism at KDD 2017.
  • Website
• Racial Disparity in Natural Language Processing: A Case Study of Social Media African-American English.
  Su Lin Blodgett and Brendan O'Connor.
  Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) workshop at KDD 2017.
• A Dataset and Classifier for Recognizing Social Media English.
  Su Lin Blodgett, Johnny Tian-Zheng Wei, and Brendan O'Connor.
  3rd Workshop on Noisy User-generated Text (WNUT) at EMNLP 2017.
  Best paper award.
• Learning to Extract Events from Knowledge Base Revisions.
  Alexander Konovalov, Benjamin Strauss, Alan Ritter and Brendan O'Connor.
  Proceedings of WWW 2017.
• Bag of What? Simple Noun Phrase Extraction for Text Analysis.

  Abram Handler, Matthew J. Denny, Hanna Wallach, and Brendan O'Connor.

  NLP+CSS Workshop at EMNLP 2016.

  • phrasemachine software
  • Slides (presenation at Text as Data conference, October 2016)
• Demographic Dialectal Variation in Social Media: A Case Study of African-American English.
  Su Lin Blodgett, Lisa Green, and Brendan O'Connor.
  Proceedings of EMNLP 2016.
• Improving Entity Ranking for Keyword Queries.
  John Foley, Brendan O'Connor, and James Allan.
  Proceedings of CIKM 2016.
  • Data
• Visualizing textual models with in-text and word-as-pixel highlighting.
  Abram Handler, Su Lin Blodgett, and Brendan O'Connor.
  At WHI 2016 - Workshop on Human Interpretability in Machine Learning (workshop at ICML 2016).
• Challenges of Visualizing Differentially Private Data.
  Dan Zhang, Michael Hay, Gerome Miklau and Brendan O'Connor.
  At TPDP 2016 - Theory and Practice of Differential Privacy (workshop at ICML 2016).
• Posterior calibration and exploratory analysis for natural language processing models.
  Khanh Nguyen and Brendan O'Connor.
  Proceedings of EMNLP 2015.
• Diffusion of lexical variation in online social media.
  Jacob Eisenstein, Brendan O'Connor, Noah A. Smith, and Eric P. Xing.
  PLOS-ONE, November 2014.
  • Also arXiv:1210.5268; an earlier version was from Oct. 2012 and poster at NIPS 2012 Workshop on Social Network and Social Media Analysis.
• Thesis: Statistical Text Analysis for Social Science.
  Brendan O'Connor.
  PhD Thesis, Carnegie Mellon University, 2014.
• MiTextExplorer: Linked brushing and mutual information for exploratory text data analysis.
  Brendan O'Connor.
  ACL Workshop on Interactive Language Learning, Visualization, and Interfaces, June 2014. (Proceedings of ACL 2014.)
  • Software website
• CMU: Arc-Factored, Discriminative Semantic Dependency Parsing.
  Sam Thomson, Brendan O'Connor, Jeffrey Flanigan, David Bamman, Jesse Dodge, Swabha Swayamdipta, Nathan Schneider, Chris Dyer, and Noah A. Smith.
  In SemEval-2014 (Proceedings of the International (COLING) Workshop on Semantic Evaluations, Dublin, Ireland, August 2014).
• Learning to Extract International Relations from Political Context.
  Brendan O'Connor, Brandon M. Stewart, and Noah A. Smith.
  Proceedings of ACL 2013.
  • Poster, slides, appendix, software
• Learning Latent Personae of Film Characters.
  David Bamman, Brendan O'Connor, and Noah A. Smith.
  Proceedings of ACL 2013.
  • Data
• Improved Part-of-Speech Tagging for Online Conversational Text with Word Clusters
  Olutobi Owoputi, Brendan O’Connor, Chris Dyer, Kevin Gimpel, Nathan Schneider and Noah A. Smith.
  Proceedings of NAACL 2013
  • Software and data
• ARKref: a rule-based coreference resolution system.
  Brendan O'Connor and Michael Heilman.
  arXiv:1310.1975, Oct 2013.
• Learning Frames from Text with an Unsupervised Latent Variable Model.
  Brendan O'Connor.
  arXiv:1307.7382, Data Analysis Project report, Machine Learning Department, CMU. July 2013.
• A framework for (under)specifying dependency syntax without overloading annotators.
  Nathan Schneider, Brendan O’Connor, Naomi Saphra, David Bamman, Manaal Faruqui, Noah A. Smith, Chris Dyer, and Jason Baldridge.
  In Linguistic Annotation Workshop, 2013.
  • [extended version arXiv:1306:2091]
• Censorship and Deletion Practices in Chinese Social Media.
  David Bamman, Brendan O'Connor, and Noah A. Smith.
  In First Monday 17.3, March 2012.
  • [web] [pdf]
  • Press coverage: BBC, New Scientist, etc.
• Computational Text Analysis for Social Science: Model Assumptions and Complexity.
  Brendan O'Connor, David Bamman, and Noah A. Smith.
  In NIPS Workshop on Comptuational Social Science and the Wisdom of Crowds, Sierra Nevada, Spain, December 2011.
• Predicting a Scientific Community's Response to an Article.
  Dani Yogatama, Michael Heilman, Brendan O'Connor, Chris Dyer, Bryan R. Routledge, and Noah A. Smith.
  In Proceedings of EMNLP 2011.
• Part-of-speech tagging for Twitter: Annotation, Features, and Experiments.
  Kevin Gimpel, Nathan Schneider, Brendan O'Connor, Dipanjan Das, Daniel Mills, Jacob Eisenstein, Michael Heilman, Dani Yogatama, Jeffrey Flanigan and Noah A. Smith.
  In ACL-2011 (short paper).
  • Data and software
• A Mixture Model of Demographic Lexical Variation.
  Brendan O'Connor, Jacob Eisenstein, Eric P. Xing, and Noah A. Smith.
  In NIPS-2010 Workshop on Machine Learning and Social Computing.
• A Latent Variable Model for Geographic Lexical Variation.
  Jacob Eisenstein, Brendan O'Connor, Noah A. Smith, and Eric P. Xing.
  In Proceedings of EMNLP 2010 (presentation).
  • Appendix
  • Data
  • Press coverage: New York Times, All Things Considered, BBC, Washington Post, Wall Street Journal, Associated Press, New Scientist, San Francisco Chronicle, Ars Technica, LA Weekly, MSNBC, etc.
• From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series.
  Brendan O'Connor, Ramnath Balasubramanyan, Bryan R. Routledge, and Noah A. Smith.
  In ICWSM-2010 (presentation).
  • Video, Slides
  • Press coverage: Pittsburgh Tribune-Review, Mashable, Ars Technica, New Scientist, CNN Tech, Fast Company, Science Now, Economic Times, BBC Radio 5 (at 13:00) and others.
• TweetMotif: Exploratory Search and Topic Summarization for Twitter.
  Brendan O'Connor, Michel Krieger, and David Ahn.
  In ICWSM-2010 (demo track).
  • Demo
• Superficial Data Analysis: Exploring Millions of Social Stereotypes.
  Brendan O'Connor and Lukas Biewald.
  In Beautiful Data, ed. Toby Segaran and Jeff Hammerbacher. O'Reilly Media. 2009.
  • Blog post, Data, Draft (with color)
• Cheap and Fast — But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks.
  Rion Snow, Brendan O’Connor, Daniel Jurafsky, and Andrew Y. Ng.
  In EMNLP-2008 (presentation).
  • Blog post, Slides, Data