Abstract
Part-of-Speech Tagging is a basic issue in the natural language processing. In this paper, we study the effect of Brill Tagger (1992) using part of the T3 Taiwanese treebank. Brill tagger is a transformation-based error-driven approach. Based on the results of other tagging method such as N-gram language model, Brill tagger learns a set of transformation rules from an annotated corpus. The learning process is error-driven in that its objective is to minimize the tagging errors computed from the comparison of the transformed results to the standard annotated corpus. Annotated corpus is often suffered from inconsistency problem, and we also study the problem using the confusing matrix. The best tagging result that we obtained is 92.80% and 85.59% for the inside test and the outside test respectively.