Raymond J. Mooney's Presentations

Conferences and Workshops

• Using Perception to Supervise Language Learning and Language to Supervise Perception
  (PPT file)
  Invited Talk, IJCAI-09 Workshop on Cross-Media Information Access and Mining, Pasadena, CA, July 13, 2009.
  ABSTRACT:
  Learning to understand and generate language and learning to interpret perceptual input are both difficult problems that usually require human-annotated training data. Our current research focuses on developing methods that can use perceptual data to supervise language learning and linguistic data to supervise the interpretion of images and video. This talk will survey three of our recent projects: 1) Learning by example to linguistically describe simulated robot soccer games, 2) Using co-training with linguistic and visual views to perform semi-supervised classification of captioned images and videos. and 3) Using closed-captions to train activity recognizers that improve video retrieval.
  Joint work with David Chen, Sonal Gupta, and Joohyun Kim
• Bottom-up Search and Transfer Learning in SRL
  (PPT file)
  Invited Talk, Joint Meeting of the 19th International Conference on Inductive Logic Programming, International Workshop on Statistical Relational Learning, and the 7th International Workshop on Mining and Learning with Graphs (ILP/SRL/MLG), Leuven, Belgium, July 2, 2009.
  ABSTRACT:
  This talk addresses two important issues motivated by of our recent research in SRL. First, is the value of data-driven, "bottom-up" search in learning the structure of SRL models. Bottom-up induction has a long history in traditional ILP; however, its use in SRL has been somewhat limited. We review recent results on several structure-learning methods for Markov Logic Networks (MLNs) that highlight the value of bottom-up search. Second, is the value of transfer learning in reducing the data and computational demands of SRL. By inducing a predicate mapping between seemingly disparate domains, effective SRL models can be efficiently learned from very small amounts of in-domain training data. For example, by transferring a model learned from data about a CS department, we have induced reasonably accurate models for IMDB movie data given training data about only a single person.
  Joint work with Lily Mihalkova and Tuyen Huynh
• Transfer Learning by Mapping and Revising Relational Knowledge
  (PPT file)
  Invited Talk, 19th Brazilian Symposium on Artificial Intelligence (SBIA-08), Salvador, Brazil, Oct. 27, 2008.
  ABSTRACT:
  Transfer Learning (TL) attempts to leverage knowledge previously acquired in a source domain to improve the accuracy and speed of learning in a related target domain. Statistical Relational Learning (SRL) concerns methods that combine the strengths of predicate logic and probabilistic graphical models in order to effectively and robustly learn and reason about complex relational data. Our recent work uses TL to improve SRL, specifically transfering learned Markov Logic Networks (MLNs), an expressive SRL formalism, to new domains. We present a complete MLN transfer system that first autonomously maps the predicates in the source MLN to the target domain and then revises the mapped knowledge to further improve its accuracy. Experimental results in several real-world domains demonstrate that our approach successfully reduces the amount of time and training data needed to learn an accurate model of a target domain over learning from scratch. A future research issue that concerns planning to learn is automatically selecting useful source domains for a given target domain or actually constructing simpler problems to use as potential sources for transfer.
  Joint work with Lily Mihalkova and Tuyen Huynh
• Learning Language from its Perceptual Context
  (PPT file)
  Invited Talk, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD-08), Antwerp, Belgium, Sept. 16, 2008.
  ABSTRACT:
  Current systems that learn to process natural language require laboriously constructed human-annotated training data. Ideally, a computer would be able to acquire language like a child by being exposed to linguistic input in the context of a relevant but ambiguous perceptual environment. As a step in this direction, we present a system that learns to sportscast simulated robot soccer games by example. The training data consists of textual human commentaries on Robocup simulation games. A set of possible alternative meanings for each comment is automatically constructed from game event traces. Our previously developed systems for learning to parse and generate natural language (KRISP and WASP) were augmented to learn from this data and then commentate novel games. The system is evaluated based on its ability to parse sentences into correct meanings and generate accurate descriptions of game events. Human evaluation was also conducted on the overall quality of the generated sportscasts and compared to human-generated commentaries.
  Joint work with David Chen, Rohit Kate, and Yuk Wah Wong.
• Learning for Semantic Parsing of Natural Language
  (PPT file)
  Reconnecting Computational Linguistics to Artificial Intelligence and Cognitive Science ("Special event")
  (PPT file)
  Invited keynote lectures presented at Computational Linguistics and Intelligent Text Processing: The 8th International Conference (CICLing-07), Mexico City, February 22, 2007.
  ABSTRACT:
  Semantic parsing is the task of mapping a natural-language sentence into a detailed formal representation of its meaning. This talk presents a summary of our research on learning semantic parsers from corpora of sentences annotated with formal representations. Our original work employed inductive-logic programming methods to learn deterministic symbolic parsers, our more recent work has applied current techniques from statistical syntactic parsing, statistical machine translation, and support vector machines using string kernels to learn more robust semantic parsers. We present results on learning to interpret natural language database queries and robot commands (Robocup coaching instructions).
  Joint work with Ruifang Ge, Rohit Kate, Yuk-Wah Wong, John Zelle, and Cynthia Thompson
• Learning for Semantic Parsing of Natural Language
  (PPT file)
  Invited keynote lecture presented at the International Joint Conference on Artificial Intelligence (IJCAI) 2005 Workshop on Grammatical Inference Applications: Successes and Future Challenges, Edinburgh, Scotland, Jul. 31, 2005.
  ABSTRACT:
  Semantic parsing is the process of mapping natural-language sentences into a formal representation of their meaning. This talk presents a summary of our research on learning semantic parsers from annotated corpora. Our original work employed inductive-logic programming methods to learn deterministic symbolic parsers, our more recent work uses either transformation rules or statistical parsing methods to learn more robust semantic parsers. We present results on learning to interpret natural language database queries and robot commands (Robocup coaching instructions).
  Joint work with Ruifang Ge, Rohit Kate, Yuk-Wah Wong, John Zelle, and Cynthia Thompson
• Diverse Ensembles for Active Learning
  (PPT file)
  presented at the 21st International Conference on Machine Learning (ICML-2004), Banff, Canada, July 2004.
  ABSTRACT:
  Query by Committee is an effective approach to selective sampling in which disagreement amongst an ensemble of hypotheses is used to select data for labeling. Query by Bagging and Query by Boosting are two practical implementations of this approach that use Bagging and Boosting, respectively, to build the committees. For effective active learning, it is critical that the committee be made up of consistent hypotheses that are very different from each other. DECORATE is a recently developed method that directly constructs such diverse committees using artificial training data. This paper introduces Active-Decorate, which uses Decorate committees to select good training examples. Extensive experimental results demonstrate that, in general, Active-DECORATE outperforms both Query by Bagging and Query by Boosting.
  Joint work with Prem Melville
• Learning Semantic Parsers: An Important but Under-Studied Problem
  (PPT file)
  presented at the AAAI 2004 Spring Symposium on Language Learning: An Interdisciplinary Perspective, Stanford, CA, March 2004.
  ABSTRACT:
  Computational systems that learn to transform natural-language sentences into semantic representations have important practical applications in building natural-language interfaces. They can also provide insight into important issues in human language acquisition. However, within AI, computational linguistics, and machine learning, there has been relatively little research on developing systems that learn such semantic parsers. This paper briefly reviews our own work in this area and presents semantic-parser acquistion as an important challenge problem for AI.

Colloquia at Universities and Research Labs

• Learning Language from its Perceptual Context
  (PPT file)
  presented at University of Texas at Dallas, Department of Computer Science, March, 6, 2008; and University of North Texas, Denton, Department of Computer Science, March 7, 2008.
  ABSTRACT:
  Current systems that learn to process natural language require laboriously constructed human-annotated training data. Ideally, a computer would be able to acquire language like a child by being exposed to linguistic input in the context of a relevant but ambiguous perceptual environment. As a step in this direction, we present a system that learns language from sportscasts of simulated soccer games. The training data consists of textual human commentaries on Robocup simulation games. A set of possible meanings for each comment is automatically constructed from game event traces. Our previously developed systems for learning to parse and generate natural language (KRISP and WASP) were augmented to learn from this data and then commentate novel games. The system is evaluated based on its ability to parse sentences into correct meanings and generate accurate descriptions of game events. Human evaluation was also conducted on the overall quality of the generated sportscasts and compared to human-generated commentaries.
  Joint work with David Chen, Rohit Kate, and Yuk Wah Wong
• Learning for Semantic Parsing of Natural Language
  (PPT file)
  presented at Carnegie Mellon University, Pittsburgh, PA, Dec. 12, 2005; and Department of Computer Science, University of Illinois at Urbana-Champaign, April 28, 2006.
  ABSTRACT:
  Semantic parsing is the task of mapping a natural-language sentence into a detailed formal representation of its meaning. This talk presents a summary of our research on learning semantic parsers from corpora of sentences annotated with formal representations. Our original work employed inductive-logic programming methods to learn deterministic symbolic parsers, our more recent work has applied current techniques from statistical syntactic parsing, machine translation, and support vector machines using string kernels to learn more robust semantic parsers. We present results on learning to interpret natural language database queries and robot commands (Robocup coaching instructions).
  Joint work with Ruifang Ge, Rohit Kate, and Yuk-Wah Wong
• Learning to Extract Proteins and their Interactions from Medline Abstracts
  (PPT file)
  presented at the University of Washington, Seattle, WA, June 22, 2005.
  ABSTRACT:
  Automatically extracting information from biomedical text holds the promise of easily consolidating large amounts of biological knowledge in computer-accessible form. This strategy is particularly attractive for extracting data on human genes from the 11 million abstracts in Medline. We have developed and evaluated a variety of learned information-extraction systems for identifying human proteins and their interactions in Medline abstracts. We will present our current best results on identifying names of human proteins using Conditional Random Fields and Relational Markov Networks. We will also present our current best results on identifying interactions between proteins using a Support Vector Machine with an underlying string kernel. Finally, we will summarize results from a recent large-scale application of our techniques, in which we mined 753,459 Medline abstracts to extract a database of 6,580 interactions between 3,737 human proteins. By merging this extracted data with existing databases, we have constructed (to our knowledge) the largest database of known human-protein interactions containing 31,609 interactions amongst 7,748 proteins.
  Joint work with Razvan Bunescu, Edward Marcotte, Ruifang Ge, Rohit Kate, Yuk-Wah Wong, and Arun Ramani.
• Learning to Extract Proteins and their Interactions from Medline Abstracts
  (PPT file)
  presented at the University of Pennsylvania 3/2/04, and Cornell University 10/21/04.
  ABSTRACT:
  Automatically extracting information from biomedical text holds the promise of easily consolidating large amounts of biological knowledge in computer-accessible form. This strategy is particularly attractive for extracting data on human genes from the 11 million abstracts in Medline. We have developed and evaluated a variety of learned information-extraction systems for identifying human proteins and their interactions in Medline abstracts. We demonstrate that machine-learning approaches using support-vector machines, maximum-entropy, and conditional random fields are able to identify human proteins with higher accuracy than several previous approaches. We also demonstrate that various rule induction methods are able to identify protein interactions more accurately than manually-developed rules. I will also discuss our recent results on collectively extracting all protein names in an abstract using Relational Markov Networks that utilize specific relations between possible protein references.
  Joint work with Razvan Bunescu, Edward Marcotte, Ruifang Ge, Rohit Kate, Yuk-Wah Wong, and Arun Ramani.
• Semi-Supervised Clustering and its Application to Document Clustering and Record Linkage
  (PPT file)
  presented at the Univ. of Maryland College Park 7/9/03, Naval Research Laboratory 12/15/03, and Google Inc. 3/25/04.
  ABSTRACT:
  Semi-supervised clustering uses a small amount of labeled data to aid the clustering of unlabeled data. It therefore learns from both labeled and unlabeled data differently than semi-supervised classification methods like co-training and transductive SVMs. We present two new algorithms that allow supervised data to bias clustering. The first approach uses labeled data to seed and constrain the k-means clustering algorithm, and has been successfully applied to clustering text documents into topic-based categories. The second approach applies EM and SVMs to labeled data to train an adaptive similarity metric for comparing textual database records, and then applies hierarchical agglomerative clustering with the trained metric to cluster unlabeled records. This approach has been successfully applied to record-linkage, the problem of identifying syntactically distinct but similar database records (such as mailing addresses or bibliographic citations) that refer to the same entity. Finally, we discuss combining the two approaches and actively selecting the most informative labeled data.
  Joint work with Sugato Basu, Misha Bilenko, and Arindam Banerjee

Philosophical/Historical/Methodological Talks

• All You Really Need to Know About Computer Science Was Learned Pursuing Artificial Intelligence
  (PPT file)(PS file)
  presented Sept. 1, 2004, Dept. of Computer Sciences, Univ. of Texas at Austin, and Cornell University 10/21/04.
  ABSTRACT:
  Most of the fundamental concepts in computing were developed by people who were trying to understand, emulate, or augment the human mind. This list of concepts includes Boolean logic, finite-state machines, formal grammars, Turing machines, linked lists, recursion, garbage collection, combinatorial search, automated theorem proving, time-shared operating systems, computer networks, graphical user interfaces, and computational complexity theory. This talk will describe how the history of all of these fundamental computing concepts is ultimately rooted in the historical pursuit of artificial intelligence. Unfortunately, subsequently, AI has become increasingly isolated from the rest of computer science, to the detriment of both. I believe the time is ripe for a re-integration of AI into the rest of computing. My goal is to start the semester with a light, mildly entertaining, and potentially controversial talk that provokes thought and discussion about the role of AI in the broader enterprise of computer science.
• Computing as an Experimental Science, or Exaggerated Formalist Rhetoric Considered Harmful
  (PPT file)
  presented Jan. 17, 2002, Dept. of Computer Sciences, Univ. of Texas at Austin.
  ABSTRACT:
  Some computing problems require an experimental rather than a formal mathematical approach to evaluating correctness or average-case time complexity. Exaggerated rhetoric of some formalists in computer science seems to deny this fundamental proposition. I will explain and defend the fundamental role of experimentation in the study of problems whose definitions involve unformalized empirical phenomena in the world. I will also discuss the lack of methodological rigor in most current experimental computer science and its connection to educational and curricular deficiencies. My goal is to start the semester with a potentially controversial but mildly entertaining talk that provokes thought and discussion about important methodological and philosophical issues regarding computing as a scientific discipline.
• AI & Atheism: AI - Mind without Mysticism: Atheism - Life, the Universe, and Everything without Mysticism
  (PPT file)
  presented Nov. 30, 2001, Forum for AI, Debate with B. Kuipers on "AI and Religion", Dept. of Computer Sciences, Univ. of Texas at Austin.
  ABSTRACT:
  Both AI and Atheism depend on a philosphy of materialism while religion relies on a philosophy of dualism. As such, I believe that belief in "strong AI" is incompatible with traditional religous beliefs.