We present a simple algorithm for clustering semantic patterns based on distributional similarity and use cluster memberships to guide semi-supervised pattern discovery. We apply ...
We present a novel framework for the discovery and representation of general semantic relationships that hold between lexical items. We propose that each such relationship can be ...
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 ev...
Automatically extracting semantic content from audio streams can be helpful in many multimedia applications. Motivated by the known limitations of traditional supervised approache...
We present a novel approach to speech processing based on the principle of pattern discovery. Our work represents a departure from traditional models of speech recognition, where t...