We present an information extraction system that decouples the tasks of finding relevant regions of text and applying extraction patterns. We create a self-trained relevant sentence classifier to identify relevant regions, and use a semantic affinity measure to automatically learn domain-relevant extraction patterns. We then distinguish primary patterns from secondary patterns and apply the patterns selectively in the relevant regions. The resulting IE system achieves good performance on the MUC-4 terrorism corpus and ProMed disease outbreak stories. This approach requires only a few seed extraction patterns and a collection of relevant and irrelevant documents for training. © 2007 Association for Computational Linguistics.
CITATION STYLE
Patwardhan, S., & Riloff, E. (2007). Effective information extraction with semantic affinity patterns and relevant regions. In EMNLP-CoNLL 2007 - Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (pp. 717–727).
Mendeley helps you to discover research relevant for your work.