Abstract
In this paper we present Chinese word segmentation algorithms based on the socalled 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.