Automatic Rule Refinement for Information Extraction

Rule-based information extraction from text is increasingly being used to populate databases and to support structured queries on unstructured text. Specification of suitable information extraction rules requires considerable skill and standard practice is to refine rules iteratively, with substantial effort. In this paper, we show that techniques developed in the context of data provenance, to determine the lineage of a tuple in a database, can be leveraged to assist in rule refinement. Specifically, given a set of extraction rules and correct and incorrect extracted data, we have developed a technique to suggest a ranked list of rule modifications that an expert rule specifier can consider. We implemented our technique in the SystemT information extraction system developed at IBM Research

• Type: Journal
• Year: 2010
• Where: PVLDB
• Authors: Bin Liu 0002, Laura Chiticariu, Vivian Chu, H. V. Jagadish, Frederick Reiss
• Keywords: Extraction Rules, Information Extraction, PVLDB 2010, Rule-based Information Extraction