Acquiring an Ontology for a Fundamental Vocabulary

Acquiring an Ontology for a Fundamental Vocabulary Francis Bond∗ and Eric Nichols ∗∗ and Sanae Fujita∗ and Takaaki Tanaka∗ * {bond,fujita,takaaki}@cslab.kecl.ntt.co.jp ** eric-n@is.naist.jp * NTT Communication Science Laboratories, Nippon Telegraph and Telephone Corporation ** Nara Advanced Institute of Science and Technology Abstract In this paper we describe the extraction of thesaurus information from parsed dictio- nary definition sentences. The main data for our experiments comes from Lexeed, a Japanese semantic dictionary, and the Hinoki treebank built on it. The dictio- nary is parsed using a head-driven phrase structure grammar of Japanese. Knowledge is extracted from the semantic representa- tion (Minimal Recursion Semantics). This makes the extraction process language inde- pendent. 1 Introduction In this paper we describe a method of acquiring a thesaurus and other useful information from a machine-readable dictionary. The research is part of a project to construct a fundamental vocabulary knowledge-base of Japanese: a resource that will include rich syntactic and semantic descriptions of the core vocabulary of Japanese. In this paper we describe the auto- matic acquisition of a thesaurus from the dic- tionary definition sentences. The basic method has a long pedigree (Copestake, 1990; Tsuru- maru et al., 1991; Rigau et al., 1997). The main difference from earlier work is that we use a mono-stratal grammar (Head-Driven Phrase Structure Grammar: Pollard and Sag (1994)) where the syntax and semantics are represented in the same structure. Our extraction can thus be done directly on the semantic output of the parser. In the first stage, we extract the thesaurus backbone of our ontology, consisting mainly of hypernym links, although other links are also extracted (e.g., domain). We also link our ∗∗Some of this research was done while the second author was visiting the NTT Communication Science Laboratories extracted thesaurus to an existing ontology of Japanese: the Goi-Taikei ontology (Ikehara et al., 1997). This allows us to use tools that ex- ploit the Goi-Taikei ontology, and also to extend it and reveal gaps. The immediate application for our ontology is in improving the performance of stochastic models for parsing (see Bond et al. (2004) for further discussion) and word sense disambigua- tion. However, this paper discusses only the construction of the ontology. We are using the Lexeed semantic database of Japanese (Kasahara et al. (2004), next sec- tion), a machine readable dictionary consisting of headwords and their definitions for the 28,000 most familiar open class words of Japanese, with all the definitions using only those 28,000 words (and some function words). We are pars- ing the definition sentences using an HPSG Japanese grammar and parser and treebanking the results into the Hinoki treebank (Bond et al., 2004). We then train a statistical model on the treebank and use it to parse the remain- ing definition sentences, and extract an ontology from them. In the next phase, we will sense tag the defini- tion sentences and use this information and the thesaurus to build a model that combines syn- tactic and semantic information. We will also produce a richer …

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