Paper: Semantic Taxonomy Induction From Heterogenous Evidence

ACL ID P06-1101
Title Semantic Taxonomy Induction From Heterogenous Evidence
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

We propose a novel algorithm for inducing seman- tic taxonomies. Previous algorithms for taxonomy induction have typically focused on independent classifiers for discovering new single relationships based on hand-constructed or automatically discov- ered textual patterns. By contrast, our algorithm flexibly incorporates evidence from multiple clas- sifiers over heterogenous relationships to optimize the entire structure of the taxonomy, using knowl- edge of a word’s coordinate terms to help in deter- mining its hypernyms, and vice versa. We apply our algorithm on the problem of sense-disambiguated noun hyponym acquisition, where we combine the predictions of hypernym and coordinate term clas- sifiers with the knowledge in a preexisting seman- tic taxonomy (WordNet 2.1). We add 10,000 nov...