Linear Reranking Model for Chinese Pinyin-to-Character Conversion

Pinyin-to-character conversion is an important task for Chinese natural language processing tasks. Previous work mainly focused on n-gram language models and machine learning approaches, or with additional hand-crafted or automatic rule-based post-processing. There are two problems unable to solve for word n-gram language model: out-of-vocabulary word recognition and long-distance grammatical constraints. In this study, we proposed a linear reranking model trying to solve these problems. Our model uses minimum error learning method to combine different sub models, which includes word and character n-gram LMs, part-of-speech tagging model and dependency model. Impact of different sub models on the conversion are fully experimented and analyzed. Results on the Lancaster Corpus of Mandarin Chinese show that our new model outperforms word n-gram language model.