초록

용어보기논문에서 용어와 풀이말을 자동 추출한 결과로, 시범 서비스 중입니다. AI-Helper

Abstract ▼ AI-Helper

Applications of a part-of-speech tagging system include ambiguity resolution in natural language processing, a post-processing in speech recognition and character recognition and so on. In this paper, we describe a Korean part-of-speech tagging system. In Korean, most word phrases are made up of mo...

Applications of a part-of-speech tagging system include ambiguity resolution in natural language processing, a post-processing in speech recognition and character recognition and so on. In this paper, we describe a Korean part-of-speech tagging system. In Korean, most word phrases are made up of more than one morpheme. However, one word phrase may be analyzed in several different ways due to morphological ambiguities. Furthermore, each analyzed result may consist of different numbers of morphemes. It causes multiple observation(word) sequences in the hidden Markov model for part-of-speech tagging. To alleviate the problems, in this paper we suggest a method for assigning a part-of-speech tag to each morpheme in Korean. The method is based on a hidden Markov model which can be trained without using any tagged corpus. To relax the amount of computation to process multiple word sequences, which arc extraordinarily occurred in Korean part-of-speech tagging, we develop a revised Viterbi algorithm for determining the most promising tag sequence using shared word sequences and virtual observations(words). Experimental results show that the model is useful in Korean part-of-speech tagging.