Exploiting Unlabeled Internal Data in Conditional Random Fields to Reduce Word Segmentation Errors for Chinese Texts

The application of text-to-speech (TTS) conversion has become widely used in recent years. Chinese TTS faces several unique difficulties. The most critical is caused by the lack of word delimiters in written Chinese. This means that Chinese word segmentation (CWS) must be the first step in Chinese TTS. Unfortunately, due to the ambiguous nature of word boundaries in Chinese, even the best CWS systems make serious segmentation errors. Incorrect sentence interpretation causes TTS errors, preventing TTS's wider use in applications such as automatic customer services or computer reader systems for the visually impaired. In this paper, we propose a novel method that exploits unlabeled internal data to reduce word segmentation errors without using external dictionaries. To demonstrate the generality of our method, we verify our system on the most widely recognized CWS evaluation tool-the SIGHAN bakeoff, which includes datasets in both traditional and simplified Chinese. These datasets are provided by four representative academics or industrial research institutes in HK, Taiwan, Mainland China, and the U.S. Our experimental results show that with only internal data and unlabeled test data, our approach reduces segmentation errors by an average of 15% compared to the traditional approach. Moreover, our approach achieves comparable performance to the best CWS systems that use external resources. Further analysis shows that our method has the potential to become more accurate as the amount of test data increases.