Research of Pinyin-To-Character conversion based on Maximum Entropy model

This paper applied Maximum Entropy (ME) model to Pinyin-To-Character (PTC) conversion instead of Hidden Markov Model (HMM) that could not include complicated and long-distance lexical information. Two ME models were built based on simple and complex templates respectively, and the complex one gave better conversion result. Furthermore, conversion trigger pair of yA → yB/cB was proposed to extract the long-distance constrain feature from the corpus; and then Average Mutual Information (AMI) was used to select conversion trigger pair features which were added to the ME model. The experiment shows that conversion error of the ME with conversion trigger pairs is reduced by 4% on a small training corpus, comparing with HMM smoothed by absolute smoothing.