Based on the sharable content object concept of advanced distributed learning, an ontology-based intelligent content object (ICO) that can automatically reason and be reused is proposed. Then, by extending the advanced distributed learning or sharable content object reference model (SCORM) specification, an interoperable model for the ICO is developed; it involves (a) adding an ontological model of general domain knowledge for intelligent tutoring systems to the SCORM specification and encapsulating the design details of the heterogeneous knowledge ontologies, (b) adding a hierarchical data structure for the current ontology element to the communication data model in the run-time environment of the SCORM specification, (c) extending the application program interface in the run-time environment of the SCORM specification to enable the ICO to query various knowledge ontologies in a consistent way, and (d) adding an Ontology section to the content aggregation model in the SCORM specification to ensure that the same ICO can be associated with different knowledge ontologies. The proposed model extends the SCORM-based courseware model from a multimedia-based structured courseware to the intelligent courseware based on a knowledge ontology and can significantly improve the overall
Automatic text classification is a research focus and core technology in information retrieval and natural language processing. Different from the traditional text classification methods (SVM, Bayesian, KNN), the class-center vector method is an important text classification method, which has the advantages of less calculation and high efficiency. However, the traditional class-center vector method for text classification has the disadvantages that the class vector is large and sparse, and its classification accuracy is not high because of the lack of semantic information. To overcome these problems, this paper proposes a novel class-center vector model for text classification using dependencies and a semantic dictionary. We respectively use WordNet English semantic dictionary and Tongyici Cilin Chinese semantic dictionary to cluster the English or Chinese feature words in the class-center vector and to significantly reduce the dimension of class-center vector, thereby realizing a new class-center vector for text classification using dependencies and a semantic dictionary. Experiments show that, compared with traditional text classification algorithms, the improved class-center vector method has lower time complexity and higher accuracy on the 20Newsgroups English corpus, Fudan and Sogou Chinese corpus. This paper is an improved version of our NLPCC2019 conference paper.
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