In the past, the learning for QA pattern relies on the labeled data, and the definition of pattern and the scoring method for the candidate answers are over simplified. The verb and noun sequence was extracted as the skeleton pattern to expand definition of QA pattern. In the learning process, a learning mechanism was established based on self-training. At first, the initial study was completed on a labeled QA pair, then the system would automatically select the reliable data for self training through searching in the Web while the system was running. The scoring method of the candidate answers was also improved by applying several heuristic rules. The experimental results show that the performance of Chinese QA system based on our method is improved significantly.

This paper presented a non-linear dimension reduction algorithm-Self-organizing Isometric Embedding (SIE) to compress high-dimensional document data. The algorithm was then validated in document clustering by being compared with the typical linear dimension reduction algorithm-Latent Semantic Indexing (LSI). Experimental results show that while significantly lowering the complexity, the performance of SIE is better than that of LSI and the benchmark.