Knowledge Mining: Proceedings of the NEMIS 2004 Final by Penelope Markellou, Maria Rigou, Spiros Sirmakessis (auth.),

By Penelope Markellou, Maria Rigou, Spiros Sirmakessis (auth.), Dr. Spiros Sirmakessis (eds.)

Text mining is a thrilling program box and a space of clinical learn that's at present below fast improvement. It makes use of thoughts from well-established medical fields (e.g. information mining, computing device studying, details retrieval, traditional language processing, case-based reasoning, facts and information administration) so that it will aid humans achieve perception, comprehend and interpret huge amounts of (usually) semi-structured and unstructured information. regardless of the advances made over the past few years, many concerns stay unresolved.

Knowledge Mining attracts upon the various key options of data administration, info mining and data discovery, meta-analysis and information visualization. in the context of medical examine, wisdom mining is mainly excited by the quantitative synthesis and visualization of study effects and findings.

The e-book offers effects from the applying of data mining recommendations in quite a few area of the educational and indystrial examine. the consequences are elevated clinical realizing besides advancements in learn caliber and price. wisdom mining items can be utilized to focus on examine possibilities, help with the presentation of "best" medical proof, facilitate study portfolio administration, in addition to, facilitate coverage environment and selection making.

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The joint analysis of the different case studies has given an adequate picture of TM applications according to the possible types of results that can be obtained, the main specifications of the sectors of applications and the type of functions. Finally it is possible to classify the applications matching the level of customisation (followed in the tools development) and the level of integration (between users and developers). This matching produces four different situations: standardisation, outsourcing, internalisation, synergism.

18. , Raffaelli, R. (2004), Text Mining applied to multilingual corpora, (in this volume). 19. , Vindigni, M. T. ), Information Extraction in the Web Era. Lecture Notes in Artificial Intelligence 2700. Springer Verlag, Berlin-Heidelberg, pp. 92–128. Understanding Text Mining: A Pragmatic Approach 49 20. Poibeau, T. (2003), Extraction Automatique d’Information: du texte brut au web semantique, Hermes – Lavoisier, Paris. 21. , Vesely, M. (2004), “From text to knowledge: document processing and visualization.

Processing of multilingual texts for the retrieval of information independent of the original language of the documents. 1 Automatic Categorisation/Classification of Documents The automatic analysis of documents is aimed at getting different types of results: (a) the classification of documents within a predefined grid of categories; (b) the clusterisation of the texts according to conceptual similarity or vocabulary; (c) the extraction of semantic information on the text; (d) the text summarisation.

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