Chinese Word Segmentation as LMR Tagging

Abstract

In this paper we present Chinese word segmentation algorithms based on the so-called LMR tagging. Our LMR taggers are implemented with the Maximum Entropy Markov Model and we then use Transformation-Based Learning to combine the results of the two LMR taggers that scan the input in opposite directions. Our system achieves F-scores of 95.9% and 91.6% on the Academia Sinica corpus and the Hong Kong City University corpus respectively.