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A new file assignment strategy of parallel I/O, which is named heuristic file sorted assignment algorithm was proposed on cluster computing system. Based on the load balancing, it assigns the files to the same disk according to the similar service time. Firstly, the files were sorted and stored at the set I in descending order in terms of their service time, then one disk of cluster node was selected randomly when the files were to be assigned, and at last the continuous files were taken orderly from the set I to the disk until the disk reached its load maximum. The experimental results show that the new strategy improves the performance by 20.2% when the load of the system is light and by 31.6% when the load is heavy. And the higher the data access rate, the more evident the improvement of the performance obtained by the heuristic file sorted assignment algorithm.

In this paper, we present a novel approach to integrate morphology information into the statistical model for CWS, which yields better accuracies than the traditional CRFs-based approach. The improvements are mainly attributed to two aspects. Firstly, the structure information within the words is integrated into the CRFs model by annotating the Chinese word corpus with morphology tags, which conveys the construction modes of Chinese words. Secondly, the training process adopts a joint CRFs model to integrate structure information with other context, which combine the morphology tag and word boundary in the same state level and complete the word segmentation and morphology tag identification complementarily. Experimental results show that the morphology information is of great use to word segmentation.