This study describes an approach to edit disfluency detection based on maximum entropy (ME) using contextual features for rich transcription of spontaneous speech. The contextual features contain word-level, chunk-level and sentence-level features for edit disfluency modeling. Due to the problem of data sparsity, word-level features are determined according to the taxonomy of the primary features of the words defined in Hownet. Chunk-level features are extracted based on mutual information of the words. Sentence-level feature are identified according to verbs and their corresponding features. The Improved Iterative Scaling (IIS) algorithm is employed to estimate the optimal weights in the maximum entropy models. Performance on edit disfluency detection and interruption point detection are conducted for evaluation. Experimental results show that the proposed method outperforms the DF-gram approach.