Semantic Taxonomy Induction from Heterogenous Evidence

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 801–808, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Semantic Taxonomy Induction from Heterogenous Evidence Rion Snow Computer Science Department Stanford University Stanford, CA 94305 rion@cs.stanford.edu Daniel Jurafsky Linguistics Department Stanford University Stanford, CA 94305 jurafsky@stanford.edu Andrew Y. Ng Computer Science Department Stanford University Stanford, CA 94305 ang@cs.stanford.edu Abstract 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 novel synsets to WordNet 2.1 at 84% precision, a rela- tive error reduction of 70% over a non-joint algo- rithm using the same component classifiers. Fi- nally, we show that a taxonomy built using our al- gorithm shows a 23% relative F-score improvement over WordNet 2.1 on an independent testset of hy- pernym pairs. 1 Introduction The goal of capturing structured relational knowl- edge about lexical terms has been the motivating force underlying many projects in lexical acquisi- tion, information extraction, and the construction of semantic taxonomies. Broad-coverage seman- tic taxonomies such as WordNet (Fellbaum, 1998) and CYC (Lenat, 1995) have been constructed by hand at great cost; while a crucial source of knowl- edge about the relations between words, these tax- onomies still suffer from sparse coverage. Many algorithms with the potential for auto- matically extending lexical resources have been proposed, including work in lexical acquisition (Riloff and Shepherd, 1997; Roark and Charniak, 1998) and in discovering instances, named enti- ties, and alternate glosses (Etzioni et al., 2005; Pasc¸a, 2005). Additionally, a wide variety of relationship-specific classifiers have been pro- posed, including pattern-based classifiers for hy- ponyms (Hearst, 1992), meronyms (Girju, 2003), synonyms (Lin et al., 2003), a variety of verb re- lations (Chklovski and Pantel, 2004), and general purpose analogy relations (Turney et al., 2003). Such classifiers use hand-written or automatically- induced patterns like Such NPy as NPx or NPy like NPx to determine, for example that NPy is a hyponym of NPx (i.e., NPy IS-A NPx). While such classifiers have achieved some degree of suc- cess, they frequently lack the global knowledge necessary to integrate their predictions into a com- plex taxonomy with multiple relations. Past work on semantic taxonomy induction in- cludes the noun hypernym hierarchy created in (Caraballo, 2001), the part-whole taxonomies in (Girju, 2003), and a great deal of recent work de- scribed in (Buitelaar et al., 2005). Such work has typically either focused …

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