Context-specific Consistencies in Information Extraction: Rule-based and Probabilistic Approaches

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BoD – Books on Demand, Aug 14, 2015 - Computers - 208 pages
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Information extraction is widely used to identify well-defined entities and relations in unstructured data. Interesting entities are often consistently structured within a certain context, especially in semi-structured texts. However, their actual composition varies and is possibly inconsistent among different contexts. Information extraction models stay behind their potential and return inferior results if they do not consider these consistencies during processing. This work presents a selection of practical and novel approaches for exploiting these context-specific consistencies in information extraction tasks. The approaches direct their attention not only to one technique, but are based on handcrafted rules as well as probabilistic models.
 

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Contents

Introduction
1
Information Extraction
11
Contextspecific Consistencies
43
UIMA Ruta
73
Knowledge Engineering Approaches
117
Machine Learning Approaches
135
Conclusion
169
Bibliography
177
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