Counter-Training in Discovery of Semantic Patterns

This paper presents a method for unsupervised discovery of semantic patterns.
Semantic patterns are useful for a variety of text understanding tasks, in particular for locating events in text for information extraction. The method builds
upon previously described approaches to
iterative unsupervised pattern acquisition.
One common characteristic of prior approaches is thatthe output ofthe algorithm
is a continuous stream of patterns, with
gradually degrading precision.
Our method differs from the previous pattern acquisition algorithms in that it introduces competition among several scenarios simultaneously. This provides natural stopping criteria for the unsupervised
learners, while maintaining good precision levels at termination. We discuss the
results of experiments with several scenarios, and examine different aspects of the
new procedure.