Abstract
The considerable development of multimedia communication goes along with an exponentially increasing volume of textual information. Information Retrieval (IR) technology provides information at a document collection level and thus it is not able to answer requests for specific pieces of information when needed. The development of intelligent tools and methods that give access to document content and extract relevant information, is more than ever a key issue for knowledge and information management. Information Extraction is one of the main research fields that attempt to fulfill this need. The IE field has been initiated by the DARPA’s MUC program (Message Understanding Conference in 1987 (MUC Proceedings). MUC has originally defined IE as the task of (1) extracting specific, well-defined pieces of information from homogeneous sets of textual documents in restricted domains (2) in order to fill the slots of pre-defined form or templates. MUC has also brought about a new evaluation paradigm: the comparison of machine-extracted information to human-produced results. MUC inspired a large amount of work in IE and has become a major reference in the text-mining field. Even in the above restrictive definition, the design of an efficient IE system with good recall (coverage) and precision (correctness) rates remains a challenging task.
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Nédellec, C. (2004). Machine Learning for Information Extraction in Genomics — State of the Art and Perspectives. In: Sirmakessis, S. (eds) Text Mining and its Applications. Studies in Fuzziness and Soft Computing, vol 138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45219-5_8
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DOI: https://doi.org/10.1007/978-3-540-45219-5_8
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