实例分析证明该模型是可行的，与已知方法相比具有较高的执行效率和较低的时间复杂度。

Experimental results show that the URSIM is feasible. Compared with the existed methods, the model is efficient and can reduce time complexity.

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一是提出并实现了词类概率模型，它具有较高的性能和较低的时间复杂度，是基于句法规则的语义分析和语言理解的基础。

The proposed Word-Class Stochastic Model (WCSM) is the basis for syntax rule based semantic parsing and spoken language understanding which has better performance.

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为迅速地检索到与当前异常情况最匹配的处理规则，设计了时间复杂度较低的异常处理规则检索算法。

For finding the most similar exception handling rule from exception rule base, the searching algorithm with low time complexity was designed.

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