Two Knives Cut Better Than One: Chinese Word Segmentation With Dual Decomposition

There are two dominant approaches to Chinese word segmentation: word-based and character-based models, each with respective strengths. Prior work has shown that gains in segmentation performance can be achieved from combining these two types of models; however, past efforts have not provided a practical technique to allow mainstream adoption. We propose a method that effectively combines the strength of both segmentation schemes using an efficient dual-decomposition algorithm for joint inference. Our method is simple and easy to implement. Experiments on SIGHAN 2003 and 2005 evaluation datasets show that our method achieves the best reported results to date on 6 out of 7 datasets.