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Text mining, also known as text data mining or knowledge discovery from textual databases, refers to the process of extracting interesting and non-trivial patterns or knowledge from text documents. Regarded by many as the next wave of knowledge discovery, text mining has very high commercial values. Last count reveals that there are more than ten high-tech(More)
This article introduces a neural architecture termed Adaptive Resonance Associative Map (ARAM) that extends unsupervised Adaptive Resonance Theory (ART) systems for rapid, yet stable, heteroassociative learning. ARAM can be visualized as two overlapping ART networks sharing a single category field. Although ARAM is simpler in architecture than another class(More)
Traditional text mining systems employ shallow parsing techniques and focus on concept extraction and taxonomic relation extraction. This paper presents a novel system called CRCTOL for mining rich semantic knowledge in the form of ontology from domain-specific text documents. By using a full text parsing technique and incorporating both statistical and(More)
This paper reports our comparative evaluation of three machine learning methods on Chinese text categorization. Whereas a wide range of methods have been applied to English text categorization, relatively few studies have been done on Chinese text categorization. Based on a reconstructed People's Daily corpus, a series of controlled experiments evaluate(More)
—Co-clustering is a commonly used technique for tapping the rich meta-information of multimedia web documents, including category, annotation, and description, for associative discovery. However, most co-clustering methods proposed for heterogeneous data do not consider the representation problem of short and noisy text and their performance is limited by(More)
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Acquisition of context poses unique challenges to mobile context-aware recommender systems. The limited resources in these systems make minimizing their context acquisition a practical need, and the uncertainty in the mobile environment makes missing and erroneous context inputs a major concern. In this paper, we propose an approach based on Bayesian(More)
We address the text content mining problem through a concept based framework by constructing a conceptual knowledge base and discovering knowledge therefrom. Defining a novel representation called the Concept Frame Graph (CFG), we propose a learning algorithm for constructing a CFG knowledge base from text documents. An interactive concept map visualization(More)