Award Abstract #0447685
CAREER: Integrating denotational meaning into probabilistic language models

NSF Org:	IIS Division of Information & Intelligent Systems
Initial Amendment Date:	March 2, 2005
Latest Amendment Date:	February 6, 2008
Award Number:	0447685
Award Instrument:	Continuing grant
Program Manager:	Tatiana D. Korelsky IIS Division of Information & Intelligent Systems CSE Directorate for Computer & Information Science & Engineering
Start Date:	May 15, 2005
Expires:	April 30, 2009 (Estimated)
Awarded Amount to Date:	$400000
Investigator(s):	William Schuler schuler@cs.umn.edu(Principal Investigator)
Sponsor:	University of Minnesota-Twin Cities 200 OAK ST SE MINNEAPOLIS, MN 55455 612/624-5599
NSF Program(s):	ROBUST INTELLIGENCE, HUMAN LANGUAGE & COMMUNICATION
Field Application(s):	0104000 Information Systems, 0116000 Human Subjects
Program Reference Code(s):	HPCC,9218,1187,1045
Program Element Code(s):	7495,7274

ABSTRACT

The purpose of this project is to develop probabilistic language models that efficiently integrate denotational semantic information into syntactic and phonological stages of recognition for use in spoken language interfaces to sensor or robotic agents. This integration is intended to allow information about the meanings or denotations of words in the agent's current environmental context to influence the probability estimates it assigns to hypothesized analyses of its input, before any recognition decisions have been made, so that the interfaced agent can favor in its search those analyses that "make sense" in its representation of the current state of the world. Since these models can be trained on the same kinds of examples that may be used to establish word meanings in the agent's lexicon, it is expected that they will be easier to adapt to changing domains than those relying exclusively on word co-occurrence statistics in fixed corpora.

A recognizer based on this model will eventually serve as a testbed for evaluating spoken language interfaces to networked scout robots in search and rescue applications and mobile manipulation robots in home care tasks or materials handling applications in joint projects with other members of the Artificial Intelligence Robotics and Vision Lab, as well as in applications that require navigating digital road maps or other geographic data in joint projects with members of the Spatial Databases group at the University of Minnesota. This model will also be adapted as a framework for teaching natural language processing concepts, providing students with a broader context in which various processing components may fit together, and inviting students to consider innovative solutions to natural language processing problems that cross the boundaries among traditional components.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Shana Watters, Tim Miller, Praveen Balachandran, William Schuler, Richard Voyles.  "Exploiting a Sensed Environment to Improve Human-Agent Communication,"  Proceedings of the 4th International Joint Conference on Autonomous Agents and Multi-Agent Systems (AA/MAS'05), Utrecht, Netherlands, 2005.,  2005,  p. 44.

Stephen Wu, Lane Schwartz, William Schuler.  "Exploiting Referential Context in Spoken Language Interfaces for Data-Poor Domains,"  Proceedings of the 2008 International Conference on Intelligent User Interfaces (IUI'08),  2008, 

William Schuler and Tim Miller.  "Integrating Denotational Meaning into a DBN Language Model,"  Proceedings of the 9th European Conference on Speech Communication and Technology (Eurospeech/Interspeech'05), Lisbon, Portugal, 2005.,  2005,  p. 34.

William Schuler, Tim Miller, Andrew Exley, Stephen Wu.  "Dynamic Evidence Models in a DBN Phone Recognizer,"  Proceedings of the 9th International Conference on Spoken Language Processing (ICSLP/Interspeech'06),  2006,  p. 1221.


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