Learning Dictionaries for Information Extraction by Multi-Level Bootstrapping

Ellen M. Riloff, University of Utah

Information extraction systems usually require two dictionaries: a semantic lexicon and a dictionary of extraction patterns for the domain. We will present a multi-level bootstrapping algorithm that generates both the semantic lexicon and extraction patterns simultaneously. As input, our technique requires only unannotated training texts and a handful of seed words for a category. We use a “mutual bootstrapping” technique to alternately select the best extraction pattern for the category and bootstrap its extractions into the semantic lexicon, which then becomes the basis for selecting the next extraction pattern. To make this approach more robust, we add a second level of bootstrapping (meta-bootstrapping) that retains only the most reliable lexicon entries produced by mutual bootstrapping and restarts the process. We evaluated this multi-level bootstrapping technique on a collection of corporate web pages and a corpus of terrorism news articles. The algorithm produced high-quality dictionaries for several semantic categories.