Name
Abstract | ||
|---|---|---|
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 -- Almaden and experimentally demonstrate its effectiveness. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.14778/1920841.1920916 | PVLDB |
Keywords | Field | DocType |
rule modification,suitable information extraction rule,systemt information extraction system,extraction rule,data provenance,automatic rule refinement,rule-based information extraction,rules iteratively,expert rule specifier,unstructured text,rule refinement,information extraction,rule based | Data mining,Rule-based system,IBM,Specifier,Ranking,Information retrieval,Tuple,Computer science,Information extraction,Database | Journal |
Volume | Issue | ISSN |
3 | 1-2 | 2150-8097 |
Citations | PageRank | References |
28 | 0.98 | 26 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bin Liu | 1 | 138 | 7.54 |
| Laura Chiticariu | 2 | 757 | 41.60 |
| Vivian Chu | 3 | 74 | 4.67 |
| H. V. Jagadish | 4 | 11141 | 2495.67 |
| Frederick R. Reiss | 5 | 371 | 17.91 |