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Effective information extraction with semantic affinity patterns and relevant regions
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Abstract:
Journal Article ; 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.
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Keyword:
Information extraction; Information retrieval; MUC-4 terrorism corpus; ProMed disease outbreak stories; Relevant regions; Semantic affinity patterns
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URL: https://collections.lib.utah.edu/ark:/87278/s6hh735g
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Exploiting role-identifying nouns and expressions for information extraction
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