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Title: Unsupervised learning of an IS-A taxonomy from a limited domain-specific corpus
Authors: Alfarone, Daniele
Davis, Jesse
Issue Date: May-2014
Publisher: Department of Computer Science, KU Leuven
Series Title: CW Reports vol:CW664
Abstract: This report addresses the problem of learning a taxonomy from a given domain-specific text corpus. We propose a novel unsupervised algorithm for this problem. Its key contributions include a clustering-based inference approach that increases recall over surface patterns and a graph-based algorithm for detecting incorrect edges that improves precision. Our system induces the taxonomy simply by analyzing the provided corpus. Thus, the learned taxonomy is focused on the concepts that are relevant for the specific corpus. An empirical evaluation on five corpora demonstrates the utility of the system.
URI: 
Publication status: published
KU Leuven publication type: IR
Appears in Collections:Informatics Section

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