Due to the limitations of language-processing tools for the Thai language, pattern-based information extraction from Thai documents requires supplementary techniques. Based on sliding-window rule application and extraction filtering, we present a framework for extracting semantic information from medical-symptom phrases with unknown boundaries in Thai unstructured-text information entries. A supervised rule learning algorithm is employed for automatic construction of information extraction rules from hand-tagged training symptom phrases. Two filtering components are introduced: one uses a classification model to predict rule application across a symptom-phrase boundary based on instantiation features of rule internal wildcards, the other uses weighted classification confidence to resolve conflicts arising from overlapping extractions. In our experimental study, we focus our attention on two basic types of symptom phrasal descriptions: one is concerned with abnormal characteristics of some observable entities and the other with human-body locations at which primitive symptoms appear. The experimental results show that the filtering components improve precision while preserving recall satisfactorily.

This paper addresses the problem of learning Bayesian belief networks (BBN) based on the minimum description length (MDL) principle. First, we give a formula of description length based on which the MDL-based procedure learns a BBN. Secondly, we point out that the difference between the MDL-based and Cooper and Herskovits procedures is essentially in the priors rather than in the approaches (MDL and Bayesian), and recommend a class of priors from which the formula is obtained. Finally, we show as a merit of using the formula that a modified version of the Chow and Liu algorithm is obtained. The modified algorithm finds a set of trees rather than a spanning tree based on the MDL principle.

A new method is developed to generate fuzzy rules from numerical data. This method consists of two algorithms: Algorithm 1 is used to identify structures of the given data set, that is, the optimal number of rules of system; Algorithm 2 is used to identify parameter of the used model. The former is belonged to unsupervised learning, and the latter is belonged to supervised learning. To identify parameters of fuzzy model, we developed a neural network which is referred to as Unsymmetrical Gaussian Function Network (UGFN). Unlike traditional fuzzy modelling methods, in the present method, a) the optimal number of rules (clusters) is determinde by input-output data pairs rather than by only output data as in sugeno's method, b) parameter identification of ghe present model is based on a like-RBF network rather than backpropagation algorithm. Our method is simple and effective because it integrates fuzzy logic with neural networks from basic network principles to neural architecture, thereby establishing an unifying framework for different fuzzy modelling methods such as one with cluster analysis or neural networks and so on.

The present study describes using the state transition type of drawing understanding framework to construct a multi-purpose drawing understanding system. This new system employs an understanding process that complies with the understanding rules, which are easily obtained by the user. The same set of user-provided rules must be used for the same type of target drawings, but for slightly different ones, fine tuning is required to obtain understanding rules. To overcome this inherent drawback in constructing drawing understanding systems, we extended the system using a newly constructed understanding rule generating support system. The resultant integrated system is based on a man-machine cooperation type interface, and can automatically generate rules from user-provided simple interactions using a graphical user interace (GUI). To obtain efficient rule generation, the system employs an inductive inference method as a learning algorithm. Map-drawing experiments were successfully carried out, and an evaluation based on a rule leaning error criterion subsequently revealed an efficient rule generation process.