Paper: Redundancy-Based Correction Of Automatically Extracted Facts

ACL ID H05-1008
Title Redundancy-Based Correction Of Automatically Extracted Facts
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2005
Authors

The accuracy of event extraction is lim- ited by a number of complicating factors, with errors compounded at all sages in- side the Information Extraction pipeline. In this paper, we present methods for re- covering automatically from errors com- mitted in the pipeline processing. Recov- ery is achieved via post-processing facts aggregated over a large collection of doc- uments, and suggesting corrections based on evidence external to the document. A further improvement is derived from prop- agating multiple, locally non-best slot fills through the pipeline. Evaluation shows that the global analysis is over 10 times more likely to suggest valid corrections to the local-only analysis than it is to suggest erroneous ones. This yields a substantial overall gain, with no supervised training.