Automated Concept Extraction from Plain Text

Boris Gelfand, Marilyn Wulfekuler, and William F. Punch III

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 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 Naive Bayes text classifier.


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