T. Eliassi-Rad & J. Shavlik (2001).
A Theory-Refinement Approach to Information Extraction. Proceedings of the Eighteenth International Conference on Machine Learning, Williamstown, MA.

This publication is available in PDF and available in postscript.

Abstract:

We investigate applying theory refinement to the task of extracting information from text. In theory refinement, partial domain knowledge (which may be incorrect) is given to a supervised learner. The provided knowledge guides the learner in its task, but the learner can refine or even discard this knowledge during training. Our supervised learner is a knowledge-based neural network that initially contains compiled prior knowledge about a particular information extraction (IE) task. The prior knowledge needs to specify the extraction slots for the specific IE task. Our approach uses generate-and-test to address the IE task. In the generation step, we produce candidate extractions by intelligently searching the space of possible extractions. In the test step, we use the trained network to judge each candidate and output those that exceed a system-selected threshold. Experiments on the CMU seminar-announcements and the Yeast subcellular-localization domains demonstrate our approach's value.

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