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We present a method for the automatic classification of text documents into a dynamically defined set of topics of interest. The proposed approach requires only a domain ontology and a set of user-defined classification topics, specified as contexts in the ontology. Our method is based on measuring the semantic similarity of the thematic graph created from(More)
The goals of the Triple Aim of health care and the goals of P4 medicine outline objectives that require a significant health informatics component. However, the goals do not provide specifications about how all of the new individual patient data will be combined in meaningful ways and with data from other sources, like epidemiological data, to promote the(More)
In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledgewhich needs to be effectively summarized to be useful. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization(More)
The amount of text that is generated every day is increasing dramatically. This tremendous volume of mostly unstructured text cannot be simply processed and perceived by computers. Therefore, efficient and effective techniques and algorithms are required to discover useful patterns. Text mining is the task of extracting meaningful information from text,(More)
Topic models, which frequently represent topics as multinomial distributions over words, have been extensively used for discovering latent topics in text corpora. Topic labeling, which aims to assign meaningful labels for discovered topics, has recently gained significant attention. In this paper, we argue that the quality of topic labeling can be improved(More)
In this paper we propose a probabilistic topic model that incorporates DBpedia knowledge into the topic model for tagging Web pages and online documents with topics discovered in them. Our method is based on integration of the DBpedia hierarchical category network with statistical topic models where DBpedia categories are considered as topics. We have(More)
Probabilistic topic models are powerful techniques which are widely used for discovering topics or semantic content from a large collection of documents. However, because topic models are entirely unsupervised, they may lead to topics that are not understandable in applications. Recently, several knowledge-based topic models have been proposed which(More)
An efficient, simple and fast low-density solvent based dispersive liquid-liquid microextraction (LDS-DLLME) followed by vortex-assisted dispersive solid phase extraction (VA-D-SPE) has been developed as a new approach for extraction and preconcentration of aflatoxin M1 in milk samples prior to its micelle enhanced spectrofluorimetic determination. In this(More)