Extracting Semantic Taxonomies of Nouns from a Korean MRD Using a Small Bootstrapping Thesaurus and a Machine Learning Approach

摘要

Most approaches for extracting hypernyms of a noun from the definition in an MRD rely on the lexico-syntactic patterns compiled by human experts. Not only these methods require high cost for compiling lexico-syntatic patterns but also it is very difficult for human experts to compile a set of lexical-syntactic patterns with a broad-coverage, because in natural languages there are various different expressions which represent the same concept. To alleviate these problems, this paper proposes a new method for extracting hypernyms of a noun from an MRD. In proposed approach, we use only syntactic(part-of-speech) patterns instead of lexico-syntactic patterns in identifying hypernyms to reduce the number of patterns while keeping their coverage broad. Our experiment shows that the classification accuracy of the proposed method is 92.37% which is significantly much better than those of previous approaches.