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AIC Seminar Series

Translating English to KR languages using inverse lambda and parameter learning

Chitta BaralArizona State University[Home Page]

Notice:  Hosted by Vinay Chaudhri

Date:  2011-06-09 at 11:00

Location:  EJ228 (SRI E Building)  (Directions)

   Abstract

Our long term goal is to develop general methodologies to translate natural language text into a formal knowledge representation (KR) language. Our approach is inspired by Montague’s path breaking thesis (1970) of viewing English as a formal language and the research in natural language semantics. Our approach is based on PCCG (Probabilistic Combinatorial Categorial Grammars), λ-calculus and statistical learning of parameters. In an initial work, we start with an initial vocabulary consisting of λ- calculus representations of a small set of words and a training corpus of sentences and their representation in a KR language. We develop a learning based system that learns the λ-calculus representation of words from this corpus and generalizes it to words of the same category. The key and novel aspect in this learning is the development of Inverse Lambda algorithms which when given λ-expressions β and γ can come up with an α such that application of α to β (or β to α) will give us γ. We augment this with learning of weights associated with multiple meanings of words. Our current system produces improved results on standard corpora on natural language interfaces for robot command and control and database queries. In an ongoing work we are able to use patterns to make guesses regarding the initial vocabulary. This together with learning of parameters allow us to develop a fully automated (without any initial vocabulary) way to translate English to designated KR languages. Our overall system is a good example of integration of results from multiple sub-fields of AI and computer science: machine learning, knowledge representation, natural language processing, λ- calculus (functional programming) and ontologies.

   Bio for Chitta Baral

Chitta Baral is a professor at the Arizona State University. He obtained his B.Tech(Hons) degree from the Indian Institute of Technology, Kharagpur in 1987 and his M.S and Ph.D degrees from the University of Maryland at College Park in 1990 and 1991 respectively. Chitta’s research interests are in the areas of Artificial Intelligence, Knowledge Representation, Cognitive Robotics, Logic Programming, Natural Language processing and application of all that to Molecular Biology. His research has been supported over the years by National Science Foundation, NASA, Science Foundation Arizona, United Space Alliance, ONR, and ARDA/DTO/IARPA. He received the NSF CAREER award in 1995. He authored the book ``Knowledge Representation, Reasoning, and Declarative Problem Solving’’ published by Cambridge University Press. He was an associate editor of the Journal of AI Research and is an area editor of the ACM Transactions on Computational Logic. His recent research focus is on temporal specification of goals, reasoning about actions and change in the multi-agent domain, combining probabilistic and logical representation and reasoning, and most recently, on natural language understanding through a learning based approach of translating natural language to knowledge representation languages.

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