We propose a cascaded linear model for joint Chinese word segmentation and partof-speech tagging. With a character-based perceptron as the core, combined with realvalued features such as language models, the cascaded model is able to efficiently utilize knowledge sources that are inconvenient to incorporate into the perceptron directly. Experiments show that the cascaded model achieves improved accuracies on both segmentation only and joint segmentation and part-of-speech tagging. On the Penn Chinese Treebank 5.0, we obtain an error reduction of 18.5% on segmentation and 12% on joint segmentation and part-of-speech tagging over the perceptron-only baseline.
The model joins word segmentation and part-of-speech tagging, which are common tasks in processing Asian languages REF .
A Cascaded Linear Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging
Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue
Computer Science