Optimizing Statistical Information Extraction Programs over Evolving Text

—Statistical information extraction (IE) programs are increasingly used to build real-world IE systems such as Alibaba, CiteSeer, Kylin, and YAGO. Current statistical IE approaches consider the text corpora underlying the extraction program to be static. However, many real-world text corpora are dynamic (documents are inserted, modified, and removed). As the corpus evolves, and IE programs must be applied repeatedly to consecutive corpus snapshots to keep extracted information up to date. Applying IE from scratch to each snapshot may be inefficient: a pair of consecutive snapshots may change very little, but unaware of this, the program must run again from scratch. In this paper, we present CRFlex, a system that efficiently executes such repeated statistical IE, by recycling previous IE results to enable incremental update. As the first step, CRFlex focuses on statistical IE programs which use a leading statistical model, Conditional Random Fields (CRFs). We show how to model pro...

• Type: Journal
• Year: 2012
• Where: ICDE
• Authors: Fei Chen, Xixuan Feng, Christopher Re, Min Wang
• Keywords: Database, Extraction Program, ICDE 2012, Inference Algorithms, World Ie