ASKNet: Creating and Evaluating Large Scale Integrated Semantic Networks

Abstract

Extracting semantic information from multiple natural language sources and combining that information into a single unified resource is an important and fundamental goal for natural language processing. Large scale resources of this kind can be useful for a wide variety of tasks including question answering, word sense disambiguation and knowledge discovery. A single resource representing the information in multiple documents can provide significantly more semantic information than is available from the documents considered independently. In this paper we describe the ASKNet system, which extracts semantic information from a large number of English texts, and combines that information into a large scale semantic network using spreading activation based techniques. Evaluation of large-scale semantic networks is a difficult problem. In order to evaluate ASKNet we have developed a novel evaluation metric and applied it to networks created from randomly chosen DUC articles. The results are highly promising:almost 80% precision for the semantic core of the networks.