Machine Learning
Webpage: http://www.cs.utexas.edu/users/ml/
Director: Raymond Mooney
Machine learning is the study of adaptive computational systems that improve performance with experience. The Machine Learning research group at UT Austin is led by Raymond J. Mooney, and our research focuses on combining empirical and knowledge-based learning techniques, including applications such as natural language acquisition, knowledge refinement, learning for planning, and recommender systems.
People
Ayan Acharya Ph.D. Student masterayan@gmail.com
Islam Beltagy Ph.D. Student beltagy@cs.utexas.edu
Yinon Bentor Ph.D. Student yinon@cs.utexas.edu
Shruti Bhosale Masters Student shruti@cs.utexas.edu
Aidan Coyne Undergraduate Student coynea90@gmail.com
Dan Garrette Ph.D. Student dhg@cs.utexas.edu
Amelia Harrison Ph.D. Student ameliaj@cs.utexas.edu
Joohyun Kim Ph.D. Student scimitar@cs.utexas.edu
Niveda Krishnamoorthy Masters Student niveda@cs.utexas.edu
Girish Malkarnenkar Masters Student girish@cs.utexas.edu
Raymond J. Mooney Professor mooney@cs.utexas.edu
Karl Pichotta Ph.D. Student pichotta@cs.utexas.edu
Subhashini Venugopalan Ph.D. Student vsub@cs.utexas.edu
Heath Vinicombe Masters Student vini@cs.utexas.edu
Publications (291)
A Formal Approach to Linking Logical Form and Vector-Space Lexical Semantics 2013
Dan Garrette, Katrin Erk, Raymond J. Mooney
Adapting Discriminative Reranking to Grounded Language Learning 2013
Joohyun Kim and Raymond J. Mooney
Generating Natural-Language Video Descriptions Using Text-Mined Knowledge 2013
Niveda Krishnamoorthy, Girish Malkarnenkar, Raymond J. Mooney, Kate Saenko, Sergio Guadarrama
Learning a Part-of-Speech Tagger from Two Hours of Annotation 2013
Dan Garrette, Jason Baldridge
Montague Meets Markov: Deep Semantics with Probabilistic Logical Form 2013
Islam Beltagy, Cuong Chau, Gemma Boleda, Dan Garrette, Katrin Erk, Raymond Mooney
Online Inference-Rule Learning from Natural-Language Extractions 2013
Sindhu Raghavan and Raymond J. Mooney
Bayesian Logic Programs for Plan Recognition and Machine Reading 2012
Sindhu Raghavan
Fast Online Lexicon Learning for Grounded Language Acquisition 2012
David L. Chen
Generative Models of Grounded Language Learning with Ambiguous Supervision 2012
Joohyun Kim
Improving Video Activity Recognition using Object Recognition and Text Mining 2012
Tanvi S. Motwani and Raymond J. Mooney
Learning Language from Ambiguous Perceptual Context 2012
David L. Chen
Learning to "Read Between the Lines" using Bayesian Logic Programs 2012
Sindhu Raghavan and Raymond J. Mooney and Hyeonseo Ku
Review Quality Aware Collaborative Filtering 2012
Sindhu Raghavan and Suriya Ganasekar and Joydeep Ghosh
Type-Supervised Hidden Markov Models for Part-of-Speech Tagging with Incomplete Tag Dictionaries 2012
Dan Garrette and Jason Baldridge
Unsupervised PCFG Induction for Grounded Language Learning with Highly Ambiguous Supervision 2012
Joohyun Kim and Raymond J. Mooney
Abductive Markov Logic for Plan Recognition 2011
Parag Singla and Raymond J. Mooney
Abductive Plan Recognition by Extending Bayesian Logic Programs 2011
Sindhu Raghavan, Raymond J. Mooney
Building a Persistent Workforce on Mechanical Turk for Multilingual Data Collection 2011
David L. Chen and William B. Dolan
Collecting Highly Parallel Data for Paraphrase Evaluation 2011
David L. Chen and William B. Dolan
Constraint Propagation for Efficient Inference in Markov Logic 2011
Tivadar Papai, Parag Singla and Henry Kautz
Cross-Cutting Models of Lexical Semantics 2011
Joseph Reisinger and Raymond Mooney
Extending Bayesian Logic Programs for Plan Recognition and Machine Reading 2011
Sindhu V. Raghavan
Fine-Grained Class Label Markup of Search Queries 2011
Joseph Reisinger and Marius Pasca
Implementing Weighted Abduction in Markov Logic 2011
James Blythe, Jerry R. Hobbs, Pedro Domingos, Rohit J. Kate, Raymond J. Mooney
Improving the Accuracy and Scalability of Discriminative Learning Methods for Markov Logic Networks 2011
Tuyen N. Huynh
Integrating Logical Representations with Probabilistic Information using Markov Logic 2011
Dan Garrette, Katrin Erk, Raymond Mooney
Learning to Interpret Natural Language Navigation Instructions from Observations 2011
David L. Chen and Raymond J. Mooney
Online Max-Margin Weight Learning for Markov Logic Networks 2011
Tuyen N. Huynh and Raymond J. Mooney
Online Structure Learning for Markov Logic Networks 2011
Tuyen N. Huynh and Raymond J. Mooney
Panning for Gold: Finding Relevant Semantic Content for Grounded Language Learning 2011
David L. Chen and Raymond J. Mooney
A Mixture Model with Sharing for Lexical Semantics 2010
Joseph Reisinger and Raymond J. Mooney
Authorship Attribution Using Probabilistic Context-Free Grammars 2010
Sindhu Raghavan, Adriana Kovashka and Raymond Mooney
Bayesian Abductive Logic Programs 2010
Sindhu Raghavan and Raymond Mooney
Cross-cutting Models of Distributional Lexical Semantics 2010
Joseph S. Reisinger
Generative Alignment and Semantic Parsing for Learning from Ambiguous Supervision 2010
Joohyun Kim and Raymond J. Mooney
Joint Entity and Relation Extraction using Card-Pyramid Parsing 2010
Rohit J. Kate and Raymond J. Mooney
Learning for Semantic Parsing Using Statistical Syntactic Parsing Techniques 2010
Ruifang Ge
Learning to Predict Readability using Diverse Linguistic Features 2010
Rohit J. Kate, Xiaoqiang Luo, Siddharth Patwardhan, Martin Franz, Radu Florian, Raymond J. Mooney, Salim Roukos and Chris Welty
Multi-Prototype Vector-Space Models of Word Meaning 2010
Joseph Reisinger, Raymond J. Mooney
Online Max-Margin Weight Learning with Markov Logic Networks 2010
Tuyen N. Huynh and Raymond J. Mooney
Spherical Topic Models 2010
Joseph Reisinger, Austin Waters, Bryan Silverthorn, and Raymond J. Mooney
Training a Multilingual Sportscaster: Using Perceptual Context to Learn Language 2010
David L. Chen, Joohyun Kim, Raymond J. Mooney
Using Closed Captions as Supervision for Video Activity Recognition 2010
Sonal Gupta, Raymond J. Mooney
Activity Retrieval in Closed Captioned Videos 2009
Sonal Gupta
Discriminative Learning with Markov Logic Networks 2009
Tuyen N. Huynh
Learning a Compositional Semantic Parser using an Existing Syntactic Parser 2009
Ruifang Ge and Raymond J. Mooney
Learning Language from Perceptual Context 2009
David L. Chen
Learning to Disambiguate Search Queries from Short Sessions 2009
Lilyana Mihalkova and Raymond Mooney
Learning with Markov Logic Networks: Transfer Learning, Structure Learning, and an Application to Web Query Disambiguation 2009
Lilyana Mihalkova
Max-Margin Weight Learning for Markov Logic Networks 2009
Tuyen N. Huynh and Raymond J. Mooney
Max-Margin Weight Learning for Markov Logic Networks 2009
Tuyen N. Huynh and Raymond J. Mooney
Probabilistic Abduction using Markov Logic Networks 2009
Rohit J. Kate and Raymond J. Mooney
Semi-supervised graph clustering: a kernel approach 2009
Brian Kulis, Sugato Basu, Inderjit Dhillon, and Raymond Mooney
Speeding up Inference In Statistical Relational Learning by Clustering Similar Query Literals 2009
Lilyana Mihalkova and Matthew Richardson
Spherical Topic Models 2009
Joseph Reisinger, Austin Waters, Bryan Silverthorn, and Raymond Mooney
Transfer Learning from Minimal Target Data by Mapping across Relational Domains 2009
Lilyana Mihalkova and Raymond Mooney
Using Closed Captions to Train Activity Recognizers that Improve Video Retrieval 2009
Sonal Gupta and Raymond Mooney
A Dependency-based Word Subsequence Kernel 2008
Rohit J. Kate
Discriminative Structure and Parameter Learning for Markov Logic Networks 2008
Tuyen N. Huynh and Raymond J. Mooney
Learning to Connect Language and Perception 2008
Raymond J. Mooney
Learning to Sportscast: A Test of Grounded Language Acquisition 2008
David L. Chen and Raymond J. Mooney
Search Query Disambiguation from Short Sessions 2008
Lilyana Mihalkova and Raymond Mooney
Transfer Learning by Mapping with Minimal Target Data 2008
Lilyana Mihalkova and Raymond J. Mooney
Transforming Meaning Representation Grammars to Improve Semantic Parsing 2008
Rohit J. Kate
Watch, Listen & Learn: Co-training on Captioned Images and Videos 2008
Sonal Gupta, Joohyun Kim, Kristen Grauman and Raymond Mooney
Bottom-Up Learning of Markov Logic Network Structure 2007
Lilyana Mihalkova and Raymond J. Mooney
Extracting Relations from Text: From Word Sequences to Dependency Paths 2007
Razvan C. Bunescu and Raymond J. Mooney
Generation by Inverting a Semantic Parser That Uses Statistical Machine Translation 2007
Yuk Wah Wong and Raymond J. Mooney
Improving Learning of Markov Logic Networks using Transfer and Bottom-Up Induction 2007
Lilyana Mihalkova
Learning for Information Extraction: From Named Entity Recognition and Disambiguation To Relation Extraction 2007
Razvan Constantin Bunescu
Learning for Semantic Parsing 2007
Raymond J. Mooney
Learning for Semantic Parsing and Natural Language Generation Using Statistical Machine Translation Techniques 2007
Yuk Wah Wong
Learning for Semantic Parsing with Kernels under Various Forms of Supervision 2007
Rohit J. Kate
Learning Language Semantics from Ambiguous Supervision 2007
Rohit J. Kate and Raymond J. Mooney
Learning Synchronous Grammars for Semantic Parsing with Lambda Calculus 2007
Yuk Wah Wong and Raymond J. Mooney
Learning to Extract Relations from the Web using Minimal Supervision 2007
Razvan C. Bunescu and Raymond J. Mooney
Mapping and Revising Markov Logic Networks for Transfer Learning 2007
Lilyana Mihalkova, Tuyen N. Huynh, Raymond J. Mooney
Multiple Instance Learning for Sparse Positive Bags 2007
Razvan C. Bunescu and Raymond J. Mooney
Semi-Supervised Learning for Semantic Parsing using Support Vector Machines 2007
Rohit J. Kate and Raymond J. Mooney
Statistical Relational Learning for Natural Language Information Extraction 2007
Razvan Bunescu and Raymond J. Mooney
Adaptive Blocking: Learning to Scale Up Record Linkage 2006
Mikhail Bilenko, Beena Kamath, Raymond J. Mooney
Discriminative Reranking for Semantic Parsing 2006
Ruifang Ge and Raymond J. Mooney
Fast and Effective Worm Fingerprinting via Machine Learning 2006
Stewart Yang, Jianping Song, Harish Rajamani, Taewon Cho, Yin Zhang and Raymond Mooney
Fast and Effective Worm Fingerprinting via Machine Learning 2006
Stewart Yang, Jianping Song, Harish Rajamani, Taewon Cho, Yin Zhang and Raymond Mooney
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
Learnable Similarity Functions and Their Application to Record Linkage and Clustering 2006
Mikhail Bilenko
Learning for Semantic Parsing with Statistical Machine Translation 2006
Yuk Wah Wong and Raymond J. Mooney
Learning Language from Perceptual Context: A Challenge Problem for AI 2006
Raymond J. Mooney
Learning Semantic Parsers Using Statistical Syntactic Parsing Techniques 2006
Ruifang Ge
Probabilistic Semi-Supervised Clustering with Constraints 2006
Sugato Basu, Mikhail Bilenko, Arindam Banerjee and Raymond J. Mooney
Subsequence Kernels for Relation Extraction 2006
Razvan Bunescu and Raymond J. Mooney
Transfer Learning with Markov Logic Networks 2006
Lilyana Mihalkova and Raymond Mooney
Using Active Relocation to Aid Reinforcement Learning 2006
Lilyana Mihalkova and Raymond Mooney
Using Encyclopedic Knowledge for Named Entity Disambiguation 2006
Razvan Bunescu and Marius Pasca
Using String-Kernels for Learning Semantic Parsers 2006
Rohit J. Kate and Raymond J. Mooney
A Kernel-based Approach to Learning Semantic Parsers 2005
Rohit J. Kate
A Shortest Path Dependency Kernel for Relation Extraction 2005
R. C. Bunescu, and Raymond J. Mooney
A Statistical Semantic Parser that Integrates Syntax and Semantics 2005
Ruifang Ge and Raymond J. Mooney
Active Learning for Probability Estimation using Jensen-Shannon Divergence 2005
P. Melville, S. M. Yang, M. Saar-Tsechansky and Raymond J. Mooney
Adaptive Product Normalization: Using Online Learning for Record Linkage in Comparison Shopping 2005
Mikhail Bilenko, Sugato Basu, and Mehran Sahami
Alignments and String Similarity in Information Integration: A Random Field Approach 2005
Mikhail Bilenko and Raymond J. Mooney
An Expected Utility Approach to Active Feature-value Acquisition 2005
P. Melville, M. Saar-Tsechansky, F. Provost and Raymond J. Mooney
Combining Bias and Variance Reduction Techniques for Regression 2005
Y. L. Suen, P. Melville and Raymond J. Mooney
Combining Bias and Variance Reduction Techniques for Regression 2005
Yuk Lai Suen, Prem Melville and Raymond J. Mooney
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
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
Creating Diverse Ensemble Classifiers to Reduce Supervision 2005
Prem Melville
Economical Active Feature-value Acquisition through Expected Utility Estimation 2005
P. Melville, M. Saar-Tsechansky, F. Provost and Raymond J. Mooney
Explaining Recommendations: Satisfaction vs. Promotion 2005
Mustafa Bilgic and Raymond J. Mooney
Learning for Collective Information Extraction 2005
Razvan C. Bunescu
Learning for Semantic Parsing Using Statistical Machine Translation Techniques 2005
Yuk Wah Wong
Learning to Transform Natural to Formal Languages 2005
Rohit J. Kate, Yuk Wah Wong and Raymond J. Mooney
Mining Knowledge from Text Using Information Extraction 2005
Raymond J. Mooney and R. Bunescu
Model-based Overlapping Clustering 2005
A. Banerjee, C. Krumpelman, S. Basu, Raymond J. Mooney and Joydeep Ghosh
Semi-supervised Clustering: Probabilistic Models, Algorithms and Experiments 2005
Sugato Basu
Semi-supervised Graph Clustering: A Kernel Approach 2005
B. Kulis, S. Basu, I. Dhillon and Raymond J. Mooney
Towards Self-Configuring Hardware for Distributed Computer Systems 2005
Jonathan Wildstrom, Peter Stone, E. Witchel, Raymond Mooney and M. Dahlin
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
A Comparison of Inference Techniques for Semi-supervised Clustering with Hidden Markov Random Fields 2004
Mikhail Bilenko and Sugato Basu
A Probabilistic Framework for Semi-Supervised Clustering 2004
Sugato Basu, Mikhail Bilenko, and Raymond J. Mooney
Active Feature-Value Acquisition for Classifier Induction 2004
Prem Melville, Maytal Saar-Tsechansky, Foster Provost, and Raymond J. Mooney
Active Feature-Value Acquisition for Classifier Induction 2004
Prem Melville, Maytal Saar-Tsechansky, Foster Provost, and Raymond J. Mooney
Active Semi-Supervision for Pairwise Constrained Clustering 2004
Sugato Basu, Arindam Banerjee, and Raymond J. Mooney
Collective Information Extraction with Relational Markov Networks 2004
Razvan Bunescu and Raymond J. Mooney
Creating Diversity in Ensembles Using Artificial Data 2004
Prem Melville and Raymond J. Mooney
Diverse Ensembles for Active Learning 2004
Prem Melville and Raymond J. Mooney
Experiments on Ensembles with Missing and Noisy Data 2004
Prem Melville, Nishit Shah, Lilyana Mihalkova, and Raymond J. Mooney
Explanation for Recommender Systems: Satisfaction vs. Promotion 2004
Mustafa Bilgic
Guiding a Reinforcement Learner with Natural Language Advice: Initial Results in RoboCup Soccer 2004
Gregory Kuhlmann, Peter Stone, Raymond J. Mooney, and Jude W. Shavlik
Integrating Constraints and Metric Learning in Semi-Supervised Clustering 2004
Mikhail Bilenko, Sugato Basu, and Raymond J. Mooney
Learnable Similarity Functions and Their Applications to Clustering and Record Linkage 2004
Mikhail Bilenko
Learning Semantic Parsers: An Important But Under-Studied Problem 2004
Raymond J. Mooney
Learning Transformation Rules for Semantic Parsing 2004
Rohit J. Kate, Yuk Wah Wong, Ruifang Ge, and Raymond J. Mooney
Relational Data Mining with Inductive Logic Programming for Link Discovery 2004
Raymond J. Mooney, P. Melville, L. R. Tang, J. Shavlik, I. Dutra and D. Page
Relational Markov Networks for Collective Information Extraction 2004
Razvan Bunescu and Raymond J. Mooney
Semi-supervised Clustering with Limited Background Knowledge 2004
Sugato Basu
Semi-supervised Clustering: Learning with Limited User Feedback 2004
Sugato Basu
Semisupervised Clustering for Intelligent User Management 2004
Sugato Basu, Mikhail Bilenko, and Raymond J. Mooney
Text Mining with Information Extraction 2004
Un Yong Nahm
Using Soft-Matching Mined Rules to Improve Information Extraction 2004
Un Yong Nahm and Raymond J. Mooney
Acquiring Word-Meaning Mappings for Natural Language Interfaces 2003
Cynthia A. Thompson and Raymond J. Mooney
Adaptive Duplicate Detection Using Learnable String Similarity Measures 2003
Mikhail Bilenko and Raymond J. Mooney
Adaptive Name-Matching in Information Integration 2003
Mikhail Bilenko, William W. Cohen, Stephen Fienberg, Raymond J. Mooney, and Pradeep Ravikumar
Associative Anaphora Resolution: A Web-Based Approach 2003
Razvan Bunescu
Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction 2003
Mary Elaine Califf and Raymond J. Mooney
Comparing and Unifying Search-Based and Similarity-Based Approaches to Semi-Supervised Clustering 2003
Sugato Basu, Mikhail Bilenko, and Raymond J. Mooney
Constructing Diverse Classifier Ensembles Using Artificial Training Examples 2003
Prem Melville and Raymond J. Mooney
Creating Diverse Ensemble Classifiers 2003
Prem Melville
Employing Trainable String Similarity Metrics for Information Integration 2003
Mikhail Bilenko and Raymond J. Mooney
Integrating Top-down and Bottom-up Approaches in Inductive Logic Programming: Applications in Natural Language Processing and Relational Data Mining 2003
Lappoon R. Tang
Learnable Similarity Functions and Their Applications to Record Linkage and Clustering 2003
Mikhail Bilenko
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
Machine Learning 2003
Raymond J. Mooney
On Evaluation and Training-Set Construction for Duplicate Detection 2003
Mikhail Bilenko and Raymond J. Mooney
Scaling Up ILP to Large Examples: Results on Link Discovery for Counter-Terrorism 2003
Lappoon R. Tang, Raymond J. Mooney, and Prem Melville
Text Mining with Information Extraction 2003
Raymond J. Mooney and Un Yong Nahm
Content-Boosted Collaborative Filtering for Improved Recommendations 2002
Prem Melville, Raymond J. Mooney, and Ramadass Nagarajan
Extracting Gene and Protein Names from Biomedical Abstracts 2002
Razvan Bunescu, Ruifang Ge, Raymond J. Mooney, Edward Marcotte, and Arun Kumar Ramani
Learning to Combine Trained Distance Metrics for Duplicate Detection in Databases 2002
Mikhail Bilenko and Raymond J. Mooney
Mining Soft-Matching Association Rules 2002
Un Yong Nahm and Raymond J. Mooney
Property-Based Feature Engineering and Selection 2002
Noppadon Kamolvilassatian
Relational Data Mining with Inductive Logic Programming for Link Discovery 2002
Raymond J. Mooney, Prem Melville, Lappoon R. Tang, Jude Shavlik, Inês de Castro Dutra, David Page, and Vítor Santos Costa
Semi-supervised Clustering by Seeding 2002
Sugato Basu, Arindam Banerjee, and Raymond J. Mooney
Text Mining with Information Extraction 2002
Un Yong Nahm and Raymond J. Mooney
Two Approaches to Handling Noisy Variation in Text Mining 2002
Un Yong Nahm, Mikhail Bilenko, and Raymond J. Mooney
Content-Boosted Collaborative Filtering 2001
Prem Melville, Raymond J. Mooney, and Ramadass Nagarajan
ELIXIR: A Library for Writing Wrappers in Java 2001
Edward Wild
Evaluating the Novelty of Text-Mined Rules using Lexical Knowledge 2001
Sugato Basu, Raymond J. Mooney, Krupakar V. Pasupuleti, and Joydeep Ghosh
Mining Soft-Matching Rules from Textual Data 2001
Un Yong Nahm and Raymond J. Mooney
Text Mining with Information Extraction 2001
Un Yong Nahm
Using Lexical Knowlege to Evaluate the Novelty of Rules Mined from Text 2001
Sugato Basu, Raymond J. Mooney, Krupakar V. Pasupuleti, and Joydeep Ghosh
Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing 2001
Lappoon R. Tang and Raymond J. Mooney
A Mutually Beneficial Integration of Data Mining and Information Extraction 2000
Un Yong Nahm and Raymond J. Mooney
Automated Construction of Database Interfaces: Integrating Statistical and Relational Learning for Semantic Parsing 2000
Lappoon R. Tang and Raymond J. Mooney
Content-Based Book Recommending Using Learning for Text Categorization 2000
Raymond J. Mooney and Loriene Roy
Integrating Abduction and Induction in Machine Learning 2000
Raymond J. Mooney
Integrating Statistical and Relational Learning for Semantic Parsing: Applications to Learning Natural Language Interfaces for Databases 2000
Lappoon R. Tang
Learning for Semantic Interpretation: Scaling Up Without Dumbing Down 2000
Raymond J. Mooney
Using Information Extraction to Aid the Discovery of Prediction Rules from Text 2000
Un Yong Nahm and Raymond J. Mooney
Active Learning for Natural Language Parsing and Information Extraction 1999
Cynthia A. Thompson, Mary Elaine Califf and Raymond J. Mooney
Automatic Construction of Semantic Lexicons for Learning Natural Language Interfaces 1999
Cynthia A. Thompson and Raymond J. Mooney
Content-Based Book Recommending Using Learning for Text Categorization 1999
Raymond J. Mooney and Loriene Roy
Relational Learning of Pattern-Match Rules for Information Extraction 1999
Mary Elaine Califf and Raymond J. Mooney
Using HTML Structure and Linked Pages to Improve Learning for Text Categorization 1999
Michael B. Cline
Advantages of Decision Lists and Implicit Negatives in Inductive Logic Programming 1998
Mary Elaine Califf and Raymond J. Mooney
An Experimental Comparison of Genetic Programming and Inductive Logic Programming on Learning Recursive List Functions 1998
Lappoon R. Tang, Mary Elaine Califf, and Raymond J. Mooney
Book Recommending Using Text Categorization with Extracted Information 1998
Raymond J. Mooney, Paul N. Bennett, and Loriene Roy
Relational Learning of Pattern-Match Rules for Information Extraction 1998
Mary Elaine Califf and Raymond J. Mooney
Relational Learning Techniques for Natural Language Information Extraction 1998
Mary Elaine Califf
Semantic Lexicon Acquisition for Learning Natural Language Interfaces 1998
Cynthia Ann Thompson
Semantic Lexicon Acquisition for Learning Natural Language Interfaces 1998
Cynthia A. Thompson and Raymond J. Mooney
Text Categorization Through Probabilistic Learning: Applications to Recommender Systems 1998
Paul N. Bennett
Theory Refinement for Bayesian Networks with Hidden Variables 1998
Sowmya Ramachandran and Raymond J. Mooney
Theory Refinement of Bayesian Networks with Hidden Variables 1998
Sowmya Ramachandran and Raymond J. Mooney
Using Multi-Strategy Learning to Improve Planning Efficiency and Quality 1998
Tara A. Estlin
An Inductive Logic Programming Method for Corpus-based Parser Construction 1997
John M. Zelle and Raymond J. Mooney
Applying ILP-based Techniques to Natural Language Information Extraction: An Experiment in Relational Learning 1997
Mary Elaine Califf and Raymond J. Mooney
Integrating Abduction and Induction in Machine Learning 1997
Raymond J. Mooney
Learning Parse and Translation Decisions From Examples With Rich Context 1997
Ulf Hermjakob
Learning Parse and Translation Decisions From Examples With Rich Context 1997
Ulf Hermjakob and Raymond J. Mooney
Learning to Improve both Efficiency and Quality of Planning 1997
Tara A. Estlin and Raymond J. Mooney
Learning to Parse Natural Language Database Queries into Logical Form 1997
Cynthia A. Thompson, Raymond J. Mooney, and Lappoon R. Tang
Parameter Revision Techniques for Bayesian Networks with Hidden Variables: An Experimental Comparison 1997
Sowmya Ramachandran and Raymond J. Mooney
Relational Learning of Pattern-Match Rules for Information Extraction 1997
Mary Elaine Califf and Raymond J. Mooney
Relational Learning Techniques for Natural Language Information Extraction 1997
Mary Elaine Califf
Semantic Lexicon Acquisition for Learning Parsers 1997
Cynthia A. Thompson and Raymond J. Mooney
A Novel Application of Theory Refinement to Student Modeling 1996
Paul Baffes and Raymond J. Mooney
Advantages of Decision Lists and Implicit Negative in Inductive Logic Programming 1996
Mary Elaine Califf and Raymond J. Mooney
Combining Symbolic and Connectionist Learning Methods to Refine Certainty-Factor Rule-Bases 1996
J. Jeffrey Mahoney
Comparative Experiments on Disambiguating Word Senses: An Illustration of the Role of Bias in Machine Learning 1996
Raymond J. Mooney
Comparative Results on Using Inductive Logic Programming for Corpus-based Parser Construction 1996
John M. Zelle and Raymond J. Mooney
Corpus-Based Lexical Acquisition For Semantic Parsing 1996
Cynthia Thompson
Hybrid Learning of Search Control for Partial-Order Planning 1996
Tara A. Estlin and Raymond J. Mooney
Inductive Logic Programming for Natural Language Processing 1996
Raymond J. Mooney
Integrating EBL and ILP to Acquire Control Rules for Planning 1996
Tara A. Estlin and Raymond J. Mooney
Integrating Explanation-Based and Inductive Learning Techniques to Acquire Search-Control for Planning 1996
Tara A. Estlin
Learning the Past Tense of English Verbs Using Inductive Logic Programming 1996
Raymond J. Mooney and Mary Elaine Califf
Learning to Parse Database Queries using Inductive Logic Programming 1996
John M. Zelle and Raymond J. Mooney
Lexical Acquisition: A Novel Machine Learning Problem 1996
Cynthia A. Thompson and Raymond J. Mooney
Multi-Strategy Learning of Search Control for Partial-Order Planning 1996
Tara A. Estlin and Raymond J. Mooney
Qualitative Multiple-Fault Diagnosis of Continuous Dynamic Systems Using Behavioral Modes 1996
Siddarth Subramanian and Raymond J. Mooney
Refinement-Based Student Modeling and Automated Bug Library Construction 1996
Paul Baffes and Raymond Mooney
Revising Bayesian Network Parameters Using Backpropagation 1996
Sowmya Ramachandran and Raymond J. Mooney
A Comparison of Two Methods Employing Inductive Logic Programming for Corpus-based Parser Constuction 1995
John M. Zelle and Raymond J. Mooney
A Preliminary PAC Analysis of Theory Revision 1995
Raymond J. Mooney
Acquisition of a Lexicon from Semantic Representations of Sentences 1995
Cynthia A. Thompson
Automated Refinement of First-Order Horn-Clause Domain Theories 1995
Bradley L. Richards and Raymond J. Mooney
Encouraging Experimental Results on Learning CNF 1995
Raymond J. Mooney
Inducing Logic Programs without Explicit Negative Examples 1995
John M. Zelle, Cynthia A. Thompson, Mary Elaine Califf, and Raymond J. Mooney
Induction of First-Order Decision Lists: Results on Learning the Past Tense of English Verbs 1995
Raymond J. Mooney and Mary Elaine Califf
Multiple-Fault Diagnosis Using General Qualitative Models with Fault Modes 1995
Siddarth Subramanian and Raymond J. Mooney
Qualitative Multiple-Fault Diagnosis of Continuous Dynamic Systems Using Behavioral Modes 1995
Siddarth Subramanian
Refinement of Bayesian Networks by Combining Connectionist and Symbolic Techniques 1995
Sowmya Ramachandran
Using Inductive Logic Programming to Automate the Construction of Natural Language Parsers 1995
John M. Zelle
A Multistrategy Approach to Theory Refinement 1994
Raymond J. Mooney and Dirk Ourston
Automatic Student Modeling and Bug Library Construction using Theory Refinement 1994
Paul T. Baffes
Combining Top-Down And Bottom-Up Techniques In Inductive Logic Programming 1994
John M. Zelle, Raymond J. Mooney, and Joshua B. Konvisser
Comparing Methods For Refining Certainty Factor Rule-Bases 1994
J. Jeffrey Mahoney and Raymond J. Mooney
Inducing Deterministic Prolog Parsers From Treebanks: A Machine Learning Approach 1994
John M. Zelle and Raymond J. Mooney
Inductive Learning For Abductive Diagnosis 1994
Cynthia A. Thompson and Raymond J. Mooney
Integrating ILP and EBL 1994
Raymond J. Mooney and John M. Zelle
Learning Qualitative Models for Systems with Multiple Operating Regions 1994
Sowmya Ramachandran, Raymond J. Mooney, and Benjamin J. Kuipers
Modifying Network Architectures For Certainty-Factor Rule-Base Revision 1994
J. Jeffrey Mahoney and Raymond J. Mooney
Multiple-Fault Diagnosis Using General Qualitative Models with Fault Modes 1994
Siddarth Subramanian and Raymond J. Mooney
Theory Refinement Combining Analytical and Empirical Methods 1994
Dirk Ourston and Raymond J. Mooney
Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule-Bases 1993
J. Jeffrey Mahoney and Raymond J. Mooney
Combining FOIL and EBG to Speed-Up Logic Programs 1993
John M. Zelle and Raymond J. Mooney
Extending Theory Refinement to M-of-N Rules 1993
Paul T. Baffes and Raymond J. Mooney
Induction Over the Unexplained: Using Overly-General Domain Theories to Aid Concept Learning 1993
Raymond J. Mooney
Inductive Learning For Abductive Diagnosis 1993
Cynthia A. Thompson
Integrating Theory and Data in Category Learning 1993
Raymond J. Mooney
Learning Search-Control Heuristics for Logic Programs: Applications to Speedup Learning and Language Acquisition 1993
John M. Zelle
Learning Semantic Grammars With Constructive Inductive Logic Programming 1993
John M. Zelle and Raymond J. Mooney
Learning to Model Students: Using Theory Refinement to Detect Misconceptions 1993
Paul T. Baffes
Symbolic Revision of Theories With M-of-N Rules 1993
Paul T. Baffes and Raymond J. Mooney
A First-Order Horn-Clause Abductive System and Its Use in Plan Recognition and Diagnosis 1992
Hwee Tou Ng and Raymond J. Mooney
A General Abductive system with application to plan recognition and diagnosis 1992
Hwee Tou Ng
Abductive Plan Recognition and Diagnosis: A Comprehensive Empirical Evaluation 1992
Hwee Tou Ng and Raymond J. Mooney
An Operator-Based Approach to First-Order Theory Revision 1992
Bradley Lance Richards
Automated Debugging of Logic Programs via Theory Revision 1992
Raymond J. Mooney and Bradley L. Richards
Automatic Abduction of Qualitative Models 1992
Bradley L. Richards, Ina Kraan, and Benjamin J. Kuipers
Batch versus Incremental Theory Refinement 1992
Raymond J. Mooney
Belief Revision in the Context of Abductive Explanation 1992
Siddarth Subramanian
Combining Symbolic and Neural Learning to Revise Probabilistic Theories 1992
J. Jeffrey Mahoney and Raymond J. Mooney
Growing Layers of Perceptrons: Introducing the Extentron Algorithm 1992
Paul T. Baffes and John M. Zelle
Learning Relations by Pathfinding 1992
Bradley L. Richards and Raymond J. Mooney
Schema acquisition from a single example 1992
W. Ahn, W. F. Brewer and Raymond J. Mooney
Speeding-up Logic Programs by Combining EBG and FOIL 1992
John M. Zelle and Raymond J. Mooney
Using Theory Revision to Model Students and Acquire Stereotypical Errors 1992
Paul T. Baffes and Raymond J. Mooney
An Efficient First-Order Horn-Clause Abduction System Based on the ATMS 1991
Hwee Tou Ng and Raymond J. Mooney
Constructive Induction in Theory Refinement 1991
Raymond J. Mooney and Dirk Ourston
First-Order Theory Revision 1991
Bradley L. Richards and Raymond J. Mooney
Improving Shared Rules in Multiple Category Domain Theories 1991
Dirk Ourston and Raymond J. Mooney
Symbolic and Neural Learning Algorithms: An Experimental Comparison 1991
J.W. Shavlik, Raymond J. Mooney and G. Towell
Theory Refinement with Noisy Data 1991
Raymond J. Mooney and Dirk Ourston
Using Explanation-Based and Empirical Methods in Theory Revision 1991
Dirk Ourston
Changing the Rules: A Comprehensive Approach to Theory Refinement 1990
D. Ourston and Raymond J. Mooney
Learning Plan Schemata From Observation: Explanation-Based Learning for Plan Recognition 1990
Raymond J. Mooney
On the Role of Coherence in Abductive Explanation 1990
Hwee Tou Ng and Raymond J. Mooney
An Experimental Comparison of Symbolic and Connectionist Learning Algorithms 1989
Raymond J. Mooney, J.W. Shavlik, G. Towell and A. Gove
Processing Issues in Comparisons of Symbolic and Connectionist Learning Systems 1989
Douglas Fisher and Kathleen McKusick and Raymond J. Mooney and Jude W. Shavlik and Geoffrey Towell
The Effect of Rule Use on the Utility of Explanation-Based Learning 1989
Raymond J. Mooney
A General Explanation-Based Learning Mechanism and its Application to Narrative Understanding 1988
Raymond J. Mooney
Generalizing the Order of Operators in Macro-Operators 1988
Raymond J. Mooney
Integrated Learning of Words and their Underlying Concepts 1987
Raymond J. Mooney
Schema Acquisition from One Example: Psychological Evidence for Explanation-Based Learning 1987
W. Ahn, Raymond J. Mooney, W.F. Brewer and G.F. DeJong
A Domain Independent Explanation-Based Generalizer 1986
Raymond J. Mooney and S.W. Bennett
Explanation-Based Learning: An Alternative View 1986
G.F. DeJong and Raymond J. Mooney
Generalizing Explanations of Narratives into Schemata 1985
Raymond J. Mooney
Generalizing Explanations of Narratives into Schemata 1985
Raymond J. Mooney
Learning Schemata for Natural Language Processing 1985
Raymond J. Mooney and Gerald F. DeJong
Areas of Interest
Abduction Active Learning Advice-taking Learners Autonomic Computing Bioinformatics Cognitive Science Connecting Language and Perception Ensemble Learning Explanation-Based Learning Inductive Learning Inductive Logic Programming Information Extraction Learning for Planning and Problem Solving Learning for Recommender Systems Learning for Semantic Parsing Lexical Semantics Machine Learning Natural Language Learning Natural Language Processing Neural-Symbolic Learning Qualitative Modeling & Diagnosis Record Linkage & Duplicate Detection Reinforcement Learning Semi-Supervised Learning Statistical Relational Learning Student Modeling for Intelligent Tutoring Systems Text Categorization and Clustering Text Data Mining Theory and Knowledge Refinement Transfer Learning Uncertain and Probabilistic Reasoning Unsupervised Learning, Clustering, and Self-Organization
Demos
Learning to Interpret Natural Language Navigation Instructions from ObservationsDavid L. Chen2012
Geoquery
Learning to Sportscast
Software/Data
KRISPER A semantic parser learning system that learn from ambiguous training examples.... 2007
WASP A semantic parser learning system that uses statistical machine translation techniques. ... 2007
ACCEL ACCEL is a general purpose system that uses abductive reasoning to construct explanations for observed intelligent pheno... 2000
BETH An ILP system that integrates traditional top-down and bottom-up approaches to combine the strengths of each and elimina... 2000
CHILL CHILL (Constructive Heuristics Induction for Language Learning) is a general approach to the problem of inducing natural... 2000
DOLPHIN DOLPHIN is a system which combines Inductive Logic Programming (i.e. FOIL) and Explanation-Based Learning (i.e. EBG) to ... 2000
FOIDL FOIDL is an ILP system for learning first-order decision lists (ordered lists of clauses each ending in a cut). It has b... 2000
FORTE FORTE (First Order Revision of Theories from Examples) is a machine learning system for modifiying a first-order Horn-cl... 2000
ML Programs A set of standard inductive classification algorithms and software for automated experimentation and system comparison w... 2000
NEITHER NEITHER is a propositional theory refinement system that will modify a incomplete or incorrect rule base so as to make i... 2000
RAPIER RAPIER is a bottom-up inductive learning system for learning information extract rules. It has been tested on several do... 2000
CHILLIN CHILLIN is an ILP system which integrates top-down search (a la FOIL), bottom-up search (a la GOLEM) and predicate inven...
ELIXIR A library for writing wrappers in Java. Download from here....