The recent growth of online information available in the form of natural language documents creates a greater need for computing systems with the ability to process those documents to simplify access to the information. One type of processing appropriate for many tasks is information extraction, a type of text skimming that retrieves specific types of information from text. Although information extraction systems have existed for two decades, these systems have generally been built by hand and contain domain specific information, making them difficult to port to other domains. A few researchers have begun to apply machine learning to information extraction tasks, but most of this work has involved applying learning to pieces of a much larger system. This paper presents a novel rule representation specific to natural language and a learning system, RAPIER, which learns information extraction rules. RAPIER takes pairs of documents and filled templates indicating the information to be extracted and learns patterns to extract fillers for the slots in the template. This proposal presents initial results on a small corpus of computer-related job postings with a preliminary version of RAPIER. Future research will involve several enhancements to RAPIER as well as more thorough testing on several domains and extension to additional natural language processing tasks. We intend to extend the rule representation and algorithm to allow for more types of constraints than are currently supported. We also plan to incorporate active learning, or sample selection, methods, specifically query by committee, into RAPIER. These methods have the potential to substantially reduce the amount of annotation required. We will explore the issue of distinguishing relevant and irrelevant messages, since currently RAPIER only extracts from the any messages given to it, assuming that all are relevant. We also intend to run much larger tests with RAPIER on multiple domains including the terrorism domain from the third and fourth Message Uncderstanding Conferences, which will allow comparison against other systems. Finally, we plan to demonstrate the generality of RAPIER`s representation and algorithm by applying it to other natural language processing tasks such as word sense disambiguation.

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unpublished. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.

@unpublished{califf:proposal97, title={Relational Learning Techniques for Natural Language Information Extraction}, author={Mary Elaine Califf}, pages={27 pages}, note={Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin}, url="http://www.cs.utexas.edu/users/ai-lab?califf:proposal97", year={1997} }