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User-Oriented Machine Learning Strategies for Information Extraction : Putting the Human Back in the Loog

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Efforts in information extraction (IE) have concen- trated on fundamental issues concerning the viability of the technology. Two of these important issues are scalability and portability. To illustrate, consider the AutoSlog system for learning a dictionary of extrac- tion patterns [Riloff, 1993]. AutoSlog automatically ac- quires extraction patterns based on training documents annotated with extraction templates, thus eliminating the need for a human programmer to encode patterns or rules for extraction. Even further, more recent ver- sions of AutoSlog acquire these patterns without explicit training data, using either relevancy signatures [Riloff, 1996] or mutual bootstrapping with a semantic lexicon [Riloff and Jones, 1999], eliminating the need for a hu- man annotator to produce training data from raw docu- ments. In short, to address the issues of scalability and portability, it has been important to find ways to get the human out of the loop, both as a programmer encoding patterns, and as an annotator producing training data.
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