Error-Driven Learning of Chinese Word Segmentation

Palmer ([4]) demonstrated how Brill's Transformation-based Error-Driven Learning can be applied to word segmentation in various languages. We present experimental results which show that such algorithms can achieve satisfactory performance even with a a very dimple initial state annotator We also present two preliminary studies, which suggest that even higher performance might be achieved if simple morphological information is available to the system, and that segmentation performance might actually be improved by combining segmentation with rudimentary part-of-speech tagging.