Machine Learning for Information Extraction

نویسنده

  • Claire Nédellec
چکیده

As an increasing amount of information becomes available in the form of electronic documents, the need to intelligently process such texts makes shallow text understanding methods such as Information Extraction (IE) particularly useful. IE has been restrictedly defined by DARPA's MUC program [MUC Proceedings] as the task of extracting specific, well-defined types of information from text in restricted domains and filling pre-defined template slots. MUC has inspired a huge amount of work in IE and has become the major reference in the field. A typical IE tasks is illustrated by the example in Figure 1 from the MUC-4 corpus that describes terrorist incidents. Even as such it is still a challenging task to build an efficient IE system with good recall (coverage) and precision (correctness) rates.

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تاریخ انتشار 2001