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Information Extraction from Multiple Syntactic Sources. Edition No. 1
VDM Publishing House, Sep 2009, Pages: 124
Information Extraction is the automatic extraction of facts from text, which includes detection of named entities, entity relations and events. Conventional approaches to Information Extraction try to find syntactic patterns based on deep processing of text, such as partial or full parsing. This book describes a novel supervised approach based on kernel methods. In this approach, customized kernels are used to match syntactic structures produced from different preprocessing phases. Individual kernels are combined into composite kernels to integrate all the information. The composite kernels can be used with various classifiers, such as Nearest Neighbor or Support Vector Machines. The main classifier being experimented is SVM due to its ability to generalize in large dimensional feature spaces. It will be shown that each level of syntactic information may contribute to Information Extraction tasks, and low level information can help to recover from errors in deep processing.
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