Combined Multiple Classifiers Based on a Stacking Algorithm and Their Application to Chinese Text Chunking

Comparing with the combined multiple classifiers based on a voting algorithm, a two-layer classifier-combination experimental framework is presented for Chinese text chunking, in which four diverse classifiers (transformation-based learning , sparse network of winnow, support vector machine, and memory based learning) a re combined with a stacking algorithm. The relevant information is incorporated into the two-layer framework as input feature vectors to construct more complete contextual models. The chunking experiments are carried out on the HIT Chinese Treebank Corpus. Experimental results show that it is an effective approach, whi ch can achieve an F score of 93.64.