Steve Minton

AAAI Conference 1999 Short Paper

Active Learning for Hierarchical Wrapper Induction

• Ion Muslea
• Steve Minton
• Craig Knoblock
• University of Southern California

As an alternative to manually writing extraction rules, we created STALKER, which is a wrapper induction algorithm that learns high-accuracy extraction rules. The major novelty introduced by STALKER is the concept of hierarchical wrapper induction: the extraction of the relevant data is performed in a hierarchical manner based on the embedded catalog tree (ECT), which is a user-provided description of the information to be extracted.

IJCAI Conference 1985 Conference Paper

Controlling Search in Flexible Parsing

• Steve Minton
• Philip J. Hayes
• Jill Fain

IJCAI Conference 1985 Conference Paper

Sefectively Generalizing Plans for Problem-Solving

• Steve Minton

Problem solving programs that generalize and save plans in order to improve their subsequent performance inevitably face the danger of being overwhelmed by an ever-increasing number of stored plans. To cope with this problem, methods must be developed for selectively learning only the most valuable aspects of a new plan. This paper describes MORRIS, a heuristic problem solver that measures the utility of plan fragments to determine whether they are worth learning. MORRIS generalizes and saves plan fragments if they are frequently used, or if they are helpful in solving difficult subproblems. Experiments are described comparing the performance of MORRIS to a less selective learning system.