Resolution of Overlapping Ambiguity Strings Based on Maximum Entropy Model

The resolution of overlapping ambiguity strings (OAS) is studied based on maximum entropy model. There are two model outputs, where either the first two characters form a word or the last two characters form a word. Features of the model include one word in context of OAS, the current OAS and word probability relation of two kinds of segmentations result. OAS in the training text is found by the combination of FMM and BMM segmentation method. After feature tagging they are used to train the maximum entropy model. The People Daily corpus of January 1998 is used in training and testing. Experimental result shows a closed test precision of 98.64% and an open test precision of 95.01%. The open test precision is improved 3.76% compared with that of the precision of common word probability method.