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.
REF presented an information extraction system that find relevant regions of text and applies extraction patterns within those regions.
Effective Information Extraction with Semantic Affinity Patterns and Relevant Regions
2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)
Computer Science