  Chinese word segmentation is a fundamental task for Chinese language
processing. The granularity mismatch problem is the main cause of the errors.
This paper showed that the binary tree representation can store outputs with
different granularity. A binary tree based framework is also designed to
overcome the granularity mismatch problem. There are two steps in this
framework, namely tree building and tree pruning. The tree pruning step is
specially designed to focus on the granularity problem. Previous work for
Chinese word segmentation such as the sequence tagging can be easily employed
in this framework. This framework can also provide quantitative error analysis
methods. The experiments showed that after using a more sophisticated tree
pruning function for a state-of-the-art conditional random field based
baseline, the error reduction can be up to 20%.
