Information Extraction
Information Extraction (IE) is a shallow form of text understanding that extracts substrings about prespecified types of entities or relationships from documents and web pages. Our work has focused on machine learning methods that induce information extractors from manually labeled training examples. Our recent work has focussed on IE for bioinformatics.

The RISE web site is a useful general information resource on IE.

Yinon Bentor Ph.D. Student yinon [at] cs utexas edu
Shruti Bhosale Formerly affiliated Masters Student shruti [at] cs utexas edu
     [Expand to show all 37][Minimize]
Online Inference-Rule Learning from Natural-Language Extractions 2013
Sindhu Raghavan and Raymond J. Mooney, In Proceedings of the 3rd Statistical Relational AI (StaRAI-13) workshop at AAAI '13, July 2013.
University of Texas at Austin KBP 2013 Slot Filling System: Bayesian Logic Programs for Textual Inference 2013
Yinon Bentor, Amelia Harrison, Shruti Bhosale, and Raymond Mooney, In Proceedings of the Sixth Text Analysis Conference (TAC 2013) 2013.
Bayesian Logic Programs for Plan Recognition and Machine Reading 2012
Sindhu Raghavan, PhD Thesis, Department of Computer Science, University of Texas at Austin. 170.
Learning to "Read Between the Lines" using Bayesian Logic Programs 2012
Sindhu Raghavan, Raymond J. Mooney, and Hyeonseo Ku, Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (ACL-2012) (2012), pp. 349--358.
Extending Bayesian Logic Programs for Plan Recognition and Machine Reading 2011
Sindhu V. Raghavan, Technical Report, PhD proposal, Department of Computer Science, The University of Texas at Austin.
Joint Entity and Relation Extraction using Card-Pyramid Parsing 2010
Rohit J. Kate and Raymond J. Mooney, In Proceedings of the Fourteenth Conference on Computational Natural Language Learning (CoNLL-2010), pp. 203--212, Uppsala, Sweden, July 2010.
Extracting Relations from Text: From Word Sequences to Dependency Paths 2007
Razvan C. Bunescu and Raymond J. Mooney, In Natural Language Processing and Text Mining, A. Kao and S. Poteet (Eds.), pp. 29-44, Berlin 2007. Springer Verlag.
Learning for Information Extraction: From Named Entity Recognition and Disambiguation To Relation Extraction 2007
Razvan Constantin Bunescu, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 150 pages. Also as Technical Report AI07-345, Artificial Intelligence Lab, University of Texas at Austin, August 2007.
Learning to Extract Relations from the Web using Minimal Supervision 2007
Razvan C. Bunescu and Raymond J. Mooney, In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL'07), Prague, Czech Republic, June 2007.
Statistical Relational Learning for Natural Language Information Extraction 2007
Razvan Bunescu and Raymond J. Mooney, In Introduction to Statistical Relational Learning, L. Getoor and B. Taskar (Eds.), pp. 535-552, Cambridge, MA 2007. MIT Press.
Integrating Co-occurrence Statistics with Information Extraction for Robust Retrieval of Protein Interactions from Medline 2006
Razvan Bunescu, Raymond Mooney, Arun Ramani and Edward Marcotte, In Proceedings of the HLT-NAACL Workshop on Linking Natural Language Processing and Biology (BioNLP'06), pp. 49-56, New York, NY, June 2006.
Learnable Similarity Functions and Their Application to Record Linkage and Clustering 2006
Mikhail Bilenko, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 136 pages.
Subsequence Kernels for Relation Extraction 2006
Razvan Bunescu and Raymond J. Mooney, In Submitted to the Ninth Conference on Natural Language Learning (CoNLL-2005), Ann Arbor, MI, July 2006. Available at url{http://www.cs.utexas.edu/users/ml/publication/ie.html}.
Using Encyclopedic Knowledge for Named Entity Disambiguation 2006
Razvan Bunescu and Marius Pasca, In Proceesings of the 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL-06), pp. 9-16, Trento, Italy 2006.
A Shortest Path Dependency Kernel for Relation Extraction 2005
R. C. Bunescu, and Raymond J. Mooney, In Proceedings of the Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP-05), pp. 724-731, Vancouver, BC, October 2005.
Comparative Experiments on Learning Information Extractors for Proteins and their Interactions 2005
Razvan Bunescu, Ruifang Ge, Rohit J. Kate, Edward M. Marcotte, Raymond J. Mooney, Arun Kumar Ramani, and Yuk Wah Wong, Artificial Intelligence in Medicine (special issue on Summarization and Information Extraction from Medical Documents), 2 (2005), pp. 139-155.
Consolidating the Set of Known Human Protein-Protein Interactions in Preparation for Large-Scale Mapping of the Human Interactome 2005
A.K. Ramani, R.C. Bunescu, Raymond J. Mooney and E.M. Marcotte, Genome Biology, Vol. 6, 5 (2005), pp. r40.
Learning for Collective Information Extraction 2005
Razvan C. Bunescu, Technical Report TR-05-02, Department of Computer Sciences, University of Texas at Austin. Ph.D. proposal.
Mining Knowledge from Text Using Information Extraction 2005
Raymond J. Mooney and R. Bunescu, SIGKDD Explorations (special issue on Text Mining and Natural Language Processing), Vol. 7, 1 (2005), pp. 3-10.
Using Biomedical Literature Mining to Consolidate the Set of Known Human Protein-Protein Interactions 2005
A. Ramani, E. Marcotte, R. Bunescu and Raymond J. Mooney, In Proceedings of the ISMB/ACL-05 Workshop of the BioLINK SIG: Linking Literature, Information and Knowledge for Biology, Detroit, MI, June 2005.
Collective Information Extraction with Relational Markov Networks 2004
Razvan Bunescu and Raymond J. Mooney, In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04), pp. 439-446, Barcelona, Spain, July 2004.
Relational Markov Networks for Collective Information Extraction 2004
Razvan Bunescu and Raymond J. Mooney, In Proceedings of the ICML-04 Workshop on Statistical Relational Learning and its Connections to Other Fields, Banff, Alberta, July 2004.
Text Mining with Information Extraction 2004
Un Yong Nahm, PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 217 pages. Also appears as Technical Report UT-AI-TR-04-311.
Using Soft-Matching Mined Rules to Improve Information Extraction 2004
Un Yong Nahm and Raymond J. Mooney, In Proceedings of the AAAI-2004 Workshop on Adaptive Text Extraction and Mining (ATEM-2004), pp. 27-32, San Jose, CA, July 2004.
Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction 2003
Mary Elaine Califf and Raymond J. Mooney, Journal of Machine Learning Research (2003), pp. 177-210.
Learning to Extract Proteins and their Interactions from Medline Abstracts 2003
Razvan Bunescu, Ruifang Ge, Rohit J. Kate, Raymond J. Mooney, Yuk Wah Wong, Edward M. Marcotte, and Arun Kumar Ramani, In Proceedings of the ICML-03 Workshop on Machine Learning in Bioinformatics, pp. 46-53, Washington, DC, August 2003.
Extracting Gene and Protein Names from Biomedical Abstracts 2002
Razvan Bunescu, Ruifang Ge, Raymond J. Mooney, Edward Marcotte, and Arun Kumar Ramani, unpublished. Unpublished Technical Note.
Property-Based Feature Engineering and Selection 2002
Noppadon Kamolvilassatian, Masters Thesis, Department of Computer Sciences, University of Texas at Austin. 85 pages.
ELIXIR: A Library for Writing Wrappers in Java 2001
Edward Wild, Undergraduate Honor Thesis, Department of Computer Sciences, University of Texas at Austin.
A Mutually Beneficial Integration of Data Mining and Information Extraction 2000
Un Yong Nahm and Raymond J. Mooney, In Proceedings of the Seventeenth National Conference on Artificial Intelligence (AAAI-00), pp. 627-632, Austin, TX, July 2000.
Active Learning for Natural Language Parsing and Information Extraction 1999
Cynthia A. Thompson, Mary Elaine Califf and Raymond J. Mooney, In Proceedings of the Sixteenth International Conference on Machine Learning (ICML-99), pp. 406-414, Bled, Slovenia, June 1999.
Relational Learning of Pattern-Match Rules for Information Extraction 1999
Mary Elaine Califf and Raymond J. Mooney, In Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI-99), pp. 328-334, Orlando, FL, July 1999.
Relational Learning of Pattern-Match Rules for Information Extraction 1998
Mary Elaine Califf and Raymond J. Mooney, In Proceedings of AAAI Spring Symposium on Applying Machine Learning to Discourse Processing, pp. 6-11, Standford, CA, March 1998.
Relational Learning Techniques for Natural Language Information Extraction 1998
Mary Elaine Califf, PhD Thesis, Department of Computer Sciences, University of Texas. 142 pages. Also appears as Artificial Intelligence Laboratory Technical Report AI 98-276.
Applying ILP-based Techniques to Natural Language Information Extraction: An Experiment in Relational Learning 1997
Mary Elaine Califf and Raymond J. Mooney, In Workshop Notes of the IJCAI-97 Workshop on Frontiers of Inductive Logic Programming, pp. 7--11, Nagoya, Japan, August 1997.
Relational Learning of Pattern-Match Rules for Information Extraction 1997
Mary Elaine Califf and Raymond J. Mooney, In Proceedings of the ACL Workshop on Natural Language Learning, pp. 9-15, Madrid, Spain, July 1997.
Relational Learning Techniques for Natural Language Information Extraction 1997
Mary Elaine Califf, unpublished. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.