부산대학교 도서관

In this paper we describe and compare two methods for automatically learning meaningful biomedical categories in Medline®. The first approach is a simple statistical method that uses part-of-speech and frequency information to extract a list of frequent headwords from noun phrases in Medline. The second method implements an alignment-based technique to learn frequent generic patterns that indicate a hyponymy/hypernymy relationship between a pair of noun phrases. We then apply these patterns to Medline to collect frequent hypernyms, potential biomedical categories. We study and compare these two alternative sets of terms to identify semantic categories in Medline. Our method is completely data-driven.