Korean Part-of-Speech Tagging using Disambiguation Rules for Ambiguous Word and Statistical Information

Abstract

A hybrid part-of-speech tagging approaches may be robust, easily extendable, and accurate because they can have the advantages of both statistical approach and rule-based approach. But conventional hybrid part-of-speech tagging systems hardly resolve some morphological ambiguities which can't be resolved by statistical information. It is because the coverage of rules is narrow. So, we define disambiguation rules for individual ambiguous word based on syntax and semantics of surround words. We select words from which the top 50% of ambiguities are occurred in Sejong corpus and build 1,814 rules for them. The accuracy of our hybrid part-of-speech tagging system using those rules is 98.28%.