Text Mining pp 41-62 | Cite as

Simple, Fast and Accurate Taxonomy Learning

Chapter
First Online:
Part of the Theory and Applications of Natural Language Processing book series (NLP)

Abstract

Although many algorithms have been developed to extract lexical resources, few organize the mined terms into taxonomies. We propose (1) a semi-supervised algorithm that uses a root term, a seed example and lexico-syntactic patterns to learn automatically from the Web hyponyms and hypernyms subordinated to the root; (2) a Web based concept positioning test to validate the learned terms and is-a relations; (3) a graph algorithm that induces from scratch the taxonomy structure of all terms and (4) a pattern-based procedure for enriching the learned taxonomies with verb-based relations. We conduct an exhaustive empirical evaluations on four different domains and show that our algorithm quickly and accurately acquires and taxonomies the knowledge. We conduct comparative studies against WordNet and existing knowledge repositories and show that our algorithm finds many additional terms and relations missing from these resources. We conduct an evaluation against other taxonomization algorithms and show how our algorithm can further enrich the taxonomies with verb-based relations.

Keywords

Taxonomic Structure Term Extraction Exist Knowledge Base Root Concept Intermediate Concept 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Computer Science DepartmentMarina del ReyUSA

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