Synthetic Word Parsing Improves Chinese Word Segmentation

Abstract

We present a novel solution to improve the performance of Chinese word segmentation (CWS) using a synthetic word parser. The parser analyses the internal structure of words, and attempts to convert out-of-vocabulary words (OOVs) into in-vocabulary fine-grained sub-words. We propose a pipeline CWS system that first predicts this fine-grained segmentation, then chunks the output to reconstruct the original word segmentation standard. We achieve competitive results on the PKU and MSR datasets, with substantial improvements in OOV recall.