The traditional market segmentation was based on ＂transcendental rationality＂ or ＂Situational Rationality＂, studies shows that it had disadvantages. This paper states the ＂Situational＂ integrated rationality hyp...The traditional market segmentation was based on ＂transcendental rationality＂ or ＂Situational Rationality＂, studies shows that it had disadvantages. This paper states the ＂Situational＂ integrated rationality hypothesis and then comes up with the market segmenting models and classification algorithm basing on this hypothesis. This algorithm combined the Rough Set theory and Neural Networks in application, which overcome the dilemma that caused complicated network structure and long training time by only using Neural Networks and influenced the classification precision caused by noise disturbance by only using Rough Set methods. Finally, the paper did a comparison experiment between the traditional method and the method we came up, the results shows that the model and algorithm has its advantage on every aspects.展开更多
To solve the complicated feature extraction and long distance dependency problem in Word Segmentation Disambiguation （WSD）, this paper proposes to apply rough sets ill WSD based on the Maximum Entropy model. Firstly...To solve the complicated feature extraction and long distance dependency problem in Word Segmentation Disambiguation （WSD）, this paper proposes to apply rough sets ill WSD based on the Maximum Entropy model. Firstly, rough set theory is applied to extract the complicated features and long distance features, even frnm noise or inconsistent corpus. Secondly, these features are added into the Maximum Entropy model, and consequently, the feature weights can be assigned according to the performance of the whole disambiguation mnltel. Finally, tile semantic lexicou is adopted to build class-hased rough set teatures to overcome data spareness. The experiment indicated that our method performed better than previous models, which got top rank in WSD in 863 Evaluation in 2003. This system ranked first and second respcetively in MSR and PKU open test in the Second International Chinese Word Segmentation Bankeoff held in 2005.展开更多
基金This paper is financial aided by the National Natural Science Foundation project in China （No. 70640008）, The National Social Science Foundation project in China （No. 05BJY043） and The Foundation Project of Inner Mongolia education office （No. N J02019）.
文摘The traditional market segmentation was based on ＂transcendental rationality＂ or ＂Situational Rationality＂, studies shows that it had disadvantages. This paper states the ＂Situational＂ integrated rationality hypothesis and then comes up with the market segmenting models and classification algorithm basing on this hypothesis. This algorithm combined the Rough Set theory and Neural Networks in application, which overcome the dilemma that caused complicated network structure and long training time by only using Neural Networks and influenced the classification precision caused by noise disturbance by only using Rough Set methods. Finally, the paper did a comparison experiment between the traditional method and the method we came up, the results shows that the model and algorithm has its advantage on every aspects.
文摘To solve the complicated feature extraction and long distance dependency problem in Word Segmentation Disambiguation （WSD）, this paper proposes to apply rough sets ill WSD based on the Maximum Entropy model. Firstly, rough set theory is applied to extract the complicated features and long distance features, even frnm noise or inconsistent corpus. Secondly, these features are added into the Maximum Entropy model, and consequently, the feature weights can be assigned according to the performance of the whole disambiguation mnltel. Finally, tile semantic lexicou is adopted to build class-hased rough set teatures to overcome data spareness. The experiment indicated that our method performed better than previous models, which got top rank in WSD in 863 Evaluation in 2003. This system ranked first and second respcetively in MSR and PKU open test in the Second International Chinese Word Segmentation Bankeoff held in 2005.
