Summary
Information extraction is the process of automatically identifying facts of interest from pieces of text, and so transforming free text into a structured database. Past work has often been successful but ad hoc, and in this paper we propose a more formal basis from which to discuss information extraction. We introduce a framework which will allow researchers to compare their methods as well as their results, and will help to reveal new insights into information extraction and text mining practices.
One problem in many information extraction applications is the creation of templates, which are textual patterns used to identify information of interest. Our framework describes formally what a template is and covers other typical information extraction tasks. We show how common search algorithms can be used to create and optimise templates automatically, using sequences of overlapping templates, and we develop heuristics that make this search feasible. Finally we demonstrate a successful implementation of the framework and apply it to a typical biological information extraction task.
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Corney, D., Byrne, E., Buxton, B., Jones, D. (2008). A Logical Framework for Template Creation and Information Extraction. In: Lin, T.Y., Xie, Y., Wasilewska, A., Liau, CJ. (eds) Data Mining: Foundations and Practice. Studies in Computational Intelligence, vol 118. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78488-3_5
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DOI: https://doi.org/10.1007/978-3-540-78488-3_5
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