A Multi-layer Chinese Word Segmentation System Optimized for Out-of-domain Tasks

State-of-the-art Chinese word segmentation systems have achieved high performance when training data and testing data are from the same domain. However, they suffer from the generalizability problem when applied on test data from different domains. We introduce a multi-layer Chinese word segmentation system which can integrate the outputs from multiple heterogeneous segmentation systems. By training a second layer of large margin classifier on top of the outputs from several Conditional Random Fields classifiers, it can utilize a small amount of in-domain training data to improve the performance. Experimental results show consistent improvement on F1 scores and OOV recall rates by applying the approach.