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One of the most serious problems that conventional knowledge management (KM) encompasses has been pointed out tardy and ineffective acquisition of knowledge. To resolve this problem, knowledge must be autonomously acquired according to its context of use by applying the technique of keyword extraction in machine
construction approach based on a modified TF-IDF, the ATF-IDF, and the well-known formal concept analysis, the FCA, algorithms. First, the approach constructs a concept lattice using keywords extracted by the ATF-IDF from collected documents to form a relationship hierarchy between all the concepts represented by the keywords. It
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
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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