Automated Concept Extraction From Plain Text
Boris Gelfand, Marilyn Wulfekuhler, William F. Punch · 1998
We describe a system for extracting concepts from unstructured text. We do this by identifying relationships between words in the text based on a lexical database and identifying groups of these words which form closely tied conceptual groups. The word relationships are used to create a directed graph, called a Semantic Relationship Graph (SRG). This SRG is a robust representation of the relationships between word senses which can be used to identify the individual concepts which occur in the text. We demonstrate the usefulness of this technique by creating a classifier based on SRGs which is considerably more accurate than a Na¨ive Bayes text classifier. 1 Introduction Mining textual information presents challenges over data mining of relational or transaction databases because text lacks any predefined fields, features or standard formats. However, mining information from unstructured textual resources such as email archives, news articles, and the World Wide Web has great potential...