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 (35)
  • Neural Networks (15)
  • Structured Learning & Prediction (15)
  • Representations & Learning (13)
  • Semantic Roles (13)
  • Applications (12)
  • Prepositions (11)
  • Integer Linear Programming (9)
  • Datasets (8)
  • Textual Inference (8)
  • Inference (7)
  • Software & Tools (7)
  • Efficient Machine Learning (6)
  • Tutorial (5)
  • Clinical Psychology & NLP (4)
  • Amortized Inference (3)
  • ML & Systems (3)
  • Visualizing NLP (3)
  • A Logic-Driven Framework for Consistency of Neural Models. Tao Li, Vivek Gupta, Maitrey Mehta and Vivek Srikumar. EMNLP, 2019.
    [pdf] [details]

  • On the Limits of Learning to Actively Learn Semantic Representations. Omri Koshorek, Gabriel Stanovsky, Yichu Zhou, Vivek Srikumar and Jonathan Berant. CoNLL, 2019.
    [pdf] [details]

  • Preparing SNACS for Subjects and Objects. Adi Shalev, Jena D. Hwang, Nathan Schneider, Vivek Srikumar, Omri Abend and Ari Rappoport. DMR workshop at ACL, 2019.
    [pdf] [details]

  • Observing Dialogue in Therapy: Categorizing and Forecasting Behavioral Codes. Jie Cao, Michael Tanana, Zac Imel, Eric Poitras, David Atkins and Vivek Srikumar. ACL, 2019.
    [pdf] [details]

  • Augmenting Neural Networks with First-order Logic. Tao Li and Vivek Srikumar. ACL, 2019.
    [pdf] [details]

  • Beyond Context: A New Perspective for Word Embeddings. Yichu Zhou and Vivek Srikumar. *SEM, 2019.
    [pdf] [details]

  • NLIZE: A Perturbation-Driven Visual Interrogation Tool for Analyzing and Interpreting Natural Language Inference Models. Shusen Liu, Zhimin Li, Tao Li, Vivek Srikumar, Valerio Pascucci and Peer-Timo Bremer. IEEE Transactions on Visualization and Computer Graphics (InfoVis), 2018.
    [pdf] [details]

  • Visual Interrogation of Attention-Based Models for Natural Language Inference and Machine Comprehension. Shusen Liu, Tao Li, Zhimin Li, Vivek Srikumar, Valerio Pascucci and Peer-Timo Bremer. EMNLP, 2018.
    [pdf] [details]

  • Newton: Gravitating Towards the Physical Limits of Crossbar Acceleration. Anirban Nag, Rajeev Balasubramonian, Vivek Srikumar, Ross Walker, Ali Shafiee, John Paul Strachan and Naveen Muralimanohar. IEEE Micro special issue on Memristor-Based Computing, 2018.
    [pdf] [details]

  • Learning to Speed Up Structured Output Prediction. Xingyuan Pan and Vivek Srikumar. ICML, 2018.
    [pdf] [details]

  • Comprehensive supersense disambiguation of English prepositions and possessives. Nathan Schneider, Jena D. Hwang, Vivek Srikumar, Jakob Prange, Austin Blodgett, Sarah Moeller, Aviram Stern, Adi Bitan and Omri Abend. ACL, 2018.
    [pdf] [details]

  • COGCOMPNLP: Your Swiss Army Knife for NLP. Daniel Khashabi, Mark Sammons, Ben Zhou, Tom Redman, Christos Christodoulopoulos, Vivek Srikumar, Nicolas Rizzolo, Lev Ratinov, Guanheng Luo, Quang do, Chen-Tse Tsai, Subhro Roy, Stephen Mayhew, Zhili Feng, John Wieting, Xiaodong Yu, Yangqiu Song, Shashank Gupta, Shyam Upadhyay, Naveen Arivazhagan, Qiang Ning, Shaoshi Ling and Dan Roth. LREC, 2018.
    [pdf] [details]

  • Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks. Aowabin Rahman, Vivek Srikumar and Amanda D. Smith. Applied Energy, 2018.
    [link] [details]

  • DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning. Min Du, Feifei Li, Guineng Zheng and Vivek Srikumar. Proceedings of 24th ACM Conference on Computer and Communications Security, 2017.
    [pdf] [details]

  • Visual Exploration of Semantic Relationships in Neural Word Embeddings. Shusen Liu, Peer-Timo Bremer, Jayaraman J. Thiagarajan, Vivek Srikumar, Bei Wang, Yarden Livnat and Valerio Pascucci. IEEE Transactions on Visualization and Computer Graphics (InfoVis), 2017.
    [pdf] [details]

  • Double trouble: the problem of construal in semantic annotation of adpositions. Jena D. Hwang, Archna Bhatia, Na-Rae Han, Tim O'Gorman, Vivek Srikumar and Nathan Schneider. *SEM, 2017.
    [pdf] [details]

  • 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]

  • 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]


Expository Articles, Unpublished Manuscripts and Tech Reports

  • Learning In Practice: Reasoning About Quantization. Annie Cherkaev, Waiming Tai, Jeff Phillips and Vivek Srikumar. arXiV preprint, 2019.
    [pdf] [details]

  • Adposition and Case Supersenses v2: Guidelines for English. Nathan Schneider, Jena D. Hwang, Archna Bhatia, Na-Rae Han, Vivek Srikumar, Tim O'Gorman, Sarah R. Moeller, Omri Abend, Austin Blodgett and Jakob Prange. arXiV preprint, 2017.
    [pdf] [details]

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

  • An Inventory of Preposition Relations. Vivek Srikumar and Dan Roth. arXiv preprint arXiv:1305.5785, 2013.
    [pdf] [details]

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

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