Natural Language => Knowledge Representation (NLKR)
Project Page
The project aims to adress a major research question that remains unanswered in NLP: whether there
are methods for getting from a robust "parseanything"
statistical parser to a semantic representation precise enough for
knowledge representation and automated reasoning, without falling
afoul of the same problems that stymied the broad application of
traditional approaches.
The task chosen for testing the developed methods is solving
logic puzzles of the sort found in the Law School
Admission Test (LSAT) and the old analytic section of the Graduate
Record Exam (GRE). Here are some
examples of logic puzzles.
 Professors:
 Ph.D. Students:
 MS Students:
 Undergrad Students:
Papers
 Iddo Lev, Bill MacCartney, Christopher D. Manning, and Roger Levy,
"Solving Logic Puzzles: From Robust Processing to Precise Semantics",
2nd Workshop on Text Meaning and Interpretation, ACL'2004
[pdf]
 Galen Andrew and Bill MacCartney, "Statistical resolution of scope ambiguity in natural language"
[pdf]
Related Work
 Patrick Blackburn and Johan Bos, Courses in Computational Semantics:
"Representation and Inference for Natural Language" and
"Working with Discourse Representation Theory", available at
www.blackburnbos.org.
 Johan Bos, DORIS 2001.
This is a major extension to the above books.
 Rui P. Chaves,
"NonRedundant Scope Disambiguation in Underspecified
Semantics",
in Balder ten Cate (Ed.) Proc. of the 8th ESSLLI Student Session, pp. 4758, 2003.
This is an extension to the plugging algorithm of B&B, which filters some logically equivalent readings during the plugging
process. You can try out the system
here.
Contact Information
Comments about the project page? Feel free to email Iddo.
