Large Scale Text Mining Approaches for Information Retrieval and Extraction
نویسندگان
چکیده
The issues for Natural Language Processing and Information Retrieval have been studied for long time but the recent availability of very large resources (Web pages, digital documents...) and the development of statistical machine learning methods exploiting annotated texts (manual encoding by crowdsourcing is a new major way) have transformed these fields. This allows not limiting these approaches to highly specialized domains and reducing the cost of their implementation. For this chapter, our aim is to present some popular text-mining statistical approaches for information retrieval and information extraction and to discuss the practical limits of actual systems that introduce challenges for future. P. Bellot (&) V. Bouvier Y.-M. Kim CNRS, Aix-Marseille Université, LSIS UMR 7296, Av. Esc. Normandie-Niemen, 13397, Marseille cedex 20, France e-mail: [email protected] V. Bouvier e-mail: [email protected] Y.-M. Kim e-mail: [email protected] L. Bonnefoy V. Bouvier F. Duvert iSmart, 565 rue M. Berthelot, 13851, Aix-en-Provence cedex 3, France e-mail: [email protected] F. Duvert e-mail: [email protected] L. Bonnefoy LIA, Université d’Avignon et des Pays de Vaucluse, Agroparc, 84911, Avignon cedex 9, France C. Faucher and L. C. Jain (eds.), Innovations in Intelligent Machines-4, Studies in Computational Intelligence 514, DOI: 10.1007/978-3-319-01866-9_1, Springer International Publishing Switzerland 2014 3
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