Toward Never Ending Language Learning.

Abstract

We report research toward a never-ending language learning system, focusing on a first implementation which learns to classify occurrences of noun phrases according to lexical categories such as “city ” and “university.” Our experiments suggest that the accuracy of classifiers produced by semi-supervised learning can be improved by coupling the learning of multiple classes based on background knowledge about relationships between the classes (e.g., ”university ” is mutually exclusive of ”company”, and is a subset of ”organization”).