Annotated corpus acquisition is a difficult problem in supervised approach. Aiming at the intention recognition task of Chinese spoken language understanding, two weakly supervised training approaches were studied. One is combining active learning with self-training, the other is co-training. A new method of acquiring two independent feature sets as two views for co-training was proposed based on spoken language understanding data in cascade frame. The two feature sets were character features of sentence and semantic class features obtained from key semantic concept recognition task. The experimental results on Chinese spoken language corpus show that the method combining active learning with self-training can minimize manual annotation compared with passive learning and active learning. Furthermore, under the premise of a few initial annotation data, co-training based on two feature sets can make the classification error rate fall in an average of 0.52% with single character feature set.

In order to ensure system security and reflect availability in authorization management, a method for querying authorization was proposed based on solvers for partial maximal satisfiability problem. Static authorization descriptions and dynamic mutually exclusive constraints were translated into hard clauses. The algorithm was adopted to update hard clauses and translate requested permissions into soft clauses. Soft clauses were effectively encoded, and the recursive algorithm was utilized to satisfy all hard clauses and as many soft clauses as possible. The experimental results show that the method can ensure system security, it follows the least privilege principle, and the query efficiency outperforms solvers for maximal satisfiability problem.