One of the articles in the latest issue of Byblos
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- added additionally, to techniques classic information retrieval, others specific to the field of artificial intelligence to help capture facts (relevant data) from the retrieved documents. By the way, and in conjunction with data mining , there are many experts who do not like the literal translation "mining data ", preferring expressions adjusted original meaning and use of Anglo-Saxon term such as" data mining "or" data discovery ", for example. The expert system described in Arti , ass, used to extract relevant information from scientific articles and technical content, is referred SEISAV (System Information Extraction Plant Protection), and system is based on the CRYSTAL (University of Massachusetts), oriented treatment of texts in English only, so it has been suitably adapted to work with the specific language of the Castilian language, and equipped with more possibilities of use. Induces (build) automatically rules textual analysis from a previous training (method of "automatic training"), but these rules can be constructed manually by an expert familiar in this type of systems, and application-specific domain (method of "Engineering for Knowledge"). The crystal system is a shell (development environment) originally designed to work under MS-DOS, marketed in Europe by Intelligent Environments. However, new environments ( AM for Windows ) relegate CRYSTAL development as a commercial product in the 90's of last century.
system operation, and some examples of use are clearly explained in the article, quite entertaining, with a level of complexity very affordable even for people without knowledge regarding previous expert systems [1
2] [3] [ 4] [5 ] [ 6] [7 ], which makes it a highly recommended reading for anyone interested in the implementation of these systems applied to document processing and information management of a significant nature (as opposed to the retrieval and treatment of informació n 'raw').