• Journal of KIISE:Software and Applications
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• v.31 no.5
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• pp.595-604
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• 2004

Automatic analysis of concepts or semantic relations from text documents enables not only an efficient acquisition of relevant information, but also a comparison of documents in the concept level. We present a multiple cause model-based approach to text analysis, where latent topics are automatically extracted from document sets and similarity between documents is measured by semantic kernels constructed from the extracted topics. In our approach, a document is assumed to be generated by various combinations of underlying topics. A topic is defined by a set of words that are related to the same topic or cooccur frequently within a document. In a network representing a multiple-cause model, each topic is identified by a group of words having high connection weights from a latent node. In order to facilitate teaming and inferences in multiple-cause models, some approximation methods are required and we utilize an approximation by Helmholtz machines. In an experiment on TDT-2 data set, we extract sets of meaningful words where each set contains some theme-specific terms. Using semantic kernels constructed from latent topics extracted by multiple cause models, we also achieve significant improvements over the basic vector space model in terms of retrieval effectiveness.

• Communications for Statistical Applications and Methods
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• v.15 no.3
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• pp.451-464
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• 2008

Six sigma is the rating that signifies "best in clas", with only 3.4 defects per million units or operations. Higher sigma quality level is generally perceived by customers as improved performance by assigning a correspondingly higher satisfaction score. The process capability indices and the sigma level $Z_{st}$ have been widely used in six sigma industries to assess process performance. Most evaluations on process capability indices focus on point estimates, which may result in unreliable assessments of process performance. In this paper, we consider statistical inference for process capability indices $C_p$, $C_{pk}$ and $C_{pm}$. Also, we study better testing procedure on assessing sigma level $Z_{st}$ and capability index $C_{pm}$, for practitioners to use in determining whether a given process is capable. The proposed method is easy to use and the decision making is more reliable. Whether a process is clearly normal or nonnormal, our bootstrap testing procedure could be applied effectively without the complexity of calculation. A numerical result based on our proposed method is illustrated.

• The Journal of the Korea Contents Association
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• v.19 no.10
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• pp.593-606
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• 2019

This study examines effective message strategies that fit with the characteristics of retargeting advertisements, based on the Construal Level Theory. First, we investigate whether the social distance that applies between interpersonal interactions can also be observable in an online environment and test the effects of social distance, depending on the type of online sites. We then examine consumers' construal level when social and temporal distances interplay, and how the effects of the messages change accordingly. As a result, social distance and message construal levels by website type were consistent with existing CLT studies. However, the hypothesis that advertising effects will be higher only when all, multidimensions' distances and construal levels of messages match, was partially supported. Also the current view, that the consumers' evaluations will show no difference to when either or both dimensions are distal, was rejected. Instead, when a discrepancy between temporal and social distance was present, the construal level of the message was more effective when congruent with the social distance out the two dimensions. Hence, it is possible to infer that the influence of psychological distance may vary depending on the dimension of distance.