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This paper presents a corpus-based approach for extracting keywords from a text written in a language that has no word boundary. Based on the concept of Thai character cluster, a Thai running text is preliminarily segmented into a sequence of inseparable units, called TCCs. To enable the handling of a large-scaled
An automatic document classifier system based on ontology and the naive Bayes classifier is proposed in this paper. The main concept is to first establish a keyword synonymous table by experts for narrowing down the range and getting the consistency of keywords. The formal concept analysis is then used for
mechanisms with a traditional indexing method. The goal is to identify a higher semantic content and more meaningful keyword combinations, considering both supervised and unsupervised techniques. Within a specific implementation both Bayesian learning as well as clustering are integrated to support a boost parameter towards
learning approach. We use a graphical model, Dynamic Conditional Random Fields (DCRFs), for training our classifier. Our approach is based on semantic analysis of text to classify the predicates describing coexpression relationship rather than detecting the presence of keywords. We compared our results of sentence
likelihood in the entire training documents where the training and test data are split randomly into k-subsets like 2/3 for training and 1/3 for test data. In addition, it also utilizes two level hierarchy structures for training documents like features from title, keywords and content with the predefined knowledge available
EMMA is an e-mail management assistant based on ripple down rules, providing a high degree of classification accuracy while simplifying the task of maintaining the consistency of the rule base. A naive Bayes algorithm is used to improve the usability of EMMA by suggesting keywords to help the user define rules. In
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