Semantic Chunk Annotation for questions using Maximum Entropy

We present a ME (Maximum Entropy) model for Semantic Chunk Annotation in a Chinese Question and Answer (Q&A) system. The model was derived from a corpus of real world questions, which are collected from some discussion groups on the Internet. The questions are supposed to be answered by other people, so the questions are very complex. The semantic chunks were introduced. Feature for the model was described and MI (mutual information) was adopted for feature selection. The training data consists of 14000 sentences and the test data consists of 4000 sentences. The result: F-score is 90.68%.