1. Research Interests
  2. Publications
  3. Tutorials

 

Research

I am interested in research questions arising from the need to manage, analyze and understand large amounts of unstructured data, particularly in textual form. My research lies in the areas of machine learning and natural language processing.

In particular, I am interested in the following broad questions:

  • Text understanding: What does text understanding mean? How can we represent and transform text into a computer-understandable form to perform machine reading and textual inference?

  • Learning Representations: How can we learn to represent inputs and outputs in real or discrete spaces that makes it easy to build predictors, especially of the structured variety?

  • Structured learning, especially with very little supervision: Many text understanding problems can be phrased as that of predicting a structured representation of text. How do we take advantage of the structure to help to efficiently learn a predictor in spite of having very little annotated data?

  • Structured prediction: Predicting structures is a combinatorial optimization problem. How can we make this faster and scalable?

Miscellany:


 

Publications

  • NLP
  • Structured Learning & Prediction
  • Applications
  • Integer Linear Programming
  • Representations & Learning
  • Semantic Roles
  • Datasets
  • Inference
  • Neural Networks
  • Prepositions
  • Tutorial
  • Textual Inference
  • Clinical Psychology & NLP
  • Software & Tools
  • Amortized Inference
  • Hardware
  • An Algebra for Feature Extraction. Vivek Srikumar. ACL, 2017.
    [pdf] [details]

  • Tutorial: Integer Linear Programming Inference for Natural Language Processing. Dan Roth and Vivek Srikumar. EACL, 2017.
    [link] [details]

  • Coping with Construals in Broad-Coverage Semantic Annotation of Adpositions. Jena D. Hwang, Archna Bhatia, Na-Rae Han, Tim O'Gorman, Vivek Srikumar and Nathan Schneider. Construction Grammar and NLU AAAI Spring Symposium, 2017.
    [pdf] [details]

  • Exploiting Sentence Similarities for Better Alignments. Tao Li and Vivek Srikumar. EMNLP, 2016.
    [pdf] [details]

  • Continuous Kernel Learning. John Moeller, Vivek Srikumar, Sarathkrishna Swaminathan, Suresh Venkatasubramanian and Dustin Webb. ECML, 2016.
    [pdf] [details]

  • A corpus of preposition supersenses. Nathan Schneider, Jena D. Hwang, Vivek Srikumar, Meredith Green, Abhijit Suresh, Kathryn Conger, Tim O'Gorman and Martha Palmer. Linguistic Annotation Workshop, 2016.
    [pdf] [details]

  • A Comparison of Natural Language Processing Methods for Automated Coding of Motivational Interviewing. Michael Tanana, Kevin Hallgren, Zac Imel, David Atkins and Vivek Srikumar. Journal of Substance Abuse Treatment, 2016.
    [link] [details]

  • ISAAC: A Convolutional Neural Network Accelerator with In-Situ Analog Arithmetic in Crossbar. Ali Shafiee, Anirban Nag, Naveen Muralimanohar, Rajeev Balasubramonian, John Paul Strachan, Miao Hu, R. Stanley Williams and Vivek Srikumar. ISCA, 2016.
    [IEEE Micro magazine Top Picks of the year honorable mention] [pdf] [details]

  • Expressiveness of Rectifier Networks. Xingyuan Pan and Vivek Srikumar. ICML, 2016.
    [pdf] [details]

  • Is Sentiment in Movies the Same as Sentiment in Psychotherapy? Comparisons Using a New Psychotherapy Sentiment Database. Michael Tanana, Aaron Dembe, Christina S. Soma, David Atkins, Zac Imel and Vivek Srikumar. Workshop on Computational Linguistics and Clinical Psychology, 2016.
    [pdf] [details]

  • EDISON: Feature Extraction for NLP. Mark Sammons, Christos Christodoulopoulos, Parisa Kordjamshidi, Daniel Khashabi, Vivek Srikumar and Dan Roth. LREC, 2016.
    [pdf] [details]

  • Learning and Inference in Structured Prediction Models. Kai-Wei Chang, Gourab Kundu, Vivek Srikumar and Dan Roth. AAAI Tutorial Forum, 2016.
    [link] [details]

  • A Hierarchy with, of, and for Preposition Supersenses. Nathan Schneider, Vivek Srikumar, Jena D. Hwang and Martha Palmer. Linguistic Annotation Workshop, 2015.
    [pdf] [details]

  • RhymeDesign: A Tool for Analyzing Sonic Devices in Poetry. Nina McCurdy, Vivek Srikumar and Miriah Meyer. Workshop on Computational Linguistics for Literature, 2015.
    [pdf] [details]

  • Recursive Neural Networks for Coding Therapist and Patient Behavior in Motivational Interviewing. Michael Tanana, Kevin Hallgren, Zac Imel, David Atkins, Padric Smyth and Vivek Srikumar. Workshop on Computational Linguistics and Clinical Psychology, 2015.
    [pdf] [details]

  • Learning Distributed Representations for Structured Output Prediction. Vivek Srikumar and Christopher D. Manning. NIPS, 2014.
    [Spotlight Presentation] [pdf] [details]

  • Modeling Biological Processes for Reading Comprehension. Jonathan Berant, Vivek Srikumar, Pei-Chun Chen, Abby Vander Linden, Brittany Harding, Brad Huang, Peter Clark and Christopher D. Manning. EMNLP, 2014.
    [Best paper award] [pdf] [details]

  • WOLFE: Strength Reduction and Approximate Programming for Probabilistic Programming. Sebastian Riedel, Sameer Singh, Vivek Srikumar, Tim Rocktäschel, Larysa Visengeriyeva and Jan Noessner. StarAI, 2014.
    [pdf] [details]

  • Correcting Grammatical Verb Errors. Alla Rozovskaya, Dan Roth and Vivek Srikumar. EACL, 2014.
    [pdf] [details]

  • The Semantics of Role Labeling. Vivek Srikumar. UIUC PhD Thesis, 2013.
    [pdf] [details]

  • Modeling Semantic Relations Expressed by Prepositions. Vivek Srikumar and Dan Roth. Transactions of ACL, 2013.
    [pdf] [details]

  • Multi-core Structural SVM Training. Kai-Wei Chang, Vivek Srikumar and Dan Roth. ECML, 2013.
    [pdf] [details]

  • Margin-based Decomposed Amortized Inference. Gourab Kundu, Vivek Srikumar and Dan Roth. ACL, 2013.
    [pdf] [details]

  • Predicting Structures in NLP: Constrained Conditional Models and Integer Linear Programming in NLP. Dan Goldwasser, Vivek Srikumar and Dan Roth. NAACL HLT 2012 Tutorials, 2012.
    [pdf] [details]

  • On Amortizing Inference Cost for Structured Prediction. Vivek Srikumar, Gourab Kundu and Dan Roth. EMNLP, 2012.
    [pdf] [details]

  • Learning Shared Body Plans. Ian Endres, Vivek Srikumar, Ming-Wei Chang and Derek Hoiem. CVPR, 2012.
    [pdf] [details]

  • An NLP Curator (or: How I Learned to Stop Worrying and Love NLP Pipelines). James Clarke, Vivek Srikumar, Mark Sammons and Dan Roth. LREC, 2012.
    [pdf] [details]

  • A Joint Model for Extended Semantic Role Labeling. Vivek Srikumar and Dan Roth. EMNLP, 2011.
    [pdf] [details]

  • Structured Output Learning with Indirect Supervision. Ming-Wei Chang, Vivek Srikumar, Dan Goldwasser and Dan Roth. ICML, 2010.
    [pdf] [details]

  • Discriminative Learning over Constrained Latent Representations. Ming-Wei Chang, Dan Goldwasser, Dan Roth and Vivek Srikumar. NAACL, 2010.
    [pdf] [details]

  • Relation Alignment for Textual Entailment Recognition. Mark Sammons, V.G.Vinod Vydiswaran, Tim Vieira, Nikhil Johri, Ming-Wei Chang, Dan Goldwasser, Vivek Srikumar, Gourab Kundu, Yuancheng Tu, Kevin Small, Josh Rule, Quang Do and Dan Roth. Text Analysis Conference (TAC), 2009.
    [pdf] [details]

  • Conceptual Search and Text Categorization. Lev Ratinov, Dan Roth and Vivek Srikumar. University of Illinois Technical Report, 2008.
    [pdf] [details]

  • Extraction of Entailed Semantic Relations Through Syntax-based Comma Resolution. Vivek Srikumar, Roi Reichart, Mark Sammons, Ari Rappoport and Dan Roth. Proc. of the Annual Meeting of the ACL, 2008.
    [pdf] [details]

  • Importance of Semantic Represenation: Dataless Classification. Ming-Wei Chang, Lev Ratinov, Dan Roth and Vivek Srikumar. Proceedings of the National Conference on Artificial Intelligence (AAAI), 2008.
    [pdf] [details]

  • Proactive Intrusion Detection. Ben Liebald, Dan Roth, Neelay Shah and Vivek Srikumar. Proceedings of the National Conference on Artificial Intelligence (AAAI), 2008.
    [pdf] [details]

  • Proactive Detection of Insider Attacks. Ben Liebald, Dan Roth, Neelay Shah and Vivek Srikumar. University of Illinois Technical Report, 2007.
    [pdf] [details]


Miscellaneous Notes

  • Soft Constraints in Integer Linear Programs. Vivek Srikumar. 2013.
    [pdf] [details]

  • A brief introduction to inference using Lagrangian Relaxation. Vivek Srikumar. 2012.
    [pdf] [details]