Collective Information Extraction with Relational Markov Networks (2004)
| Citations: | 52 - 4 self |
BibTeX
@MISC{Bunescu04collectiveinformation,
author = {R. Bunescu and Razvan Bunescu and R. J. Mooney},
title = {Collective Information Extraction with Relational Markov Networks},
year = {2004}
}
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Abstract
Most information extraction (IE) systems treat separate potential extractions as independent. However, in many cases, considering inuences between dierent potential extractions could improve overall accuracy. Statistical methods based on undirected graphical models, such as conditional random elds (CRFs), have been shown to be an eective approach to learning accurate IE systems. We present a new IE method that employs Relational Markov Networks (a generalization of CRFs), which can represent arbitrary dependencies between extractions. This allows for \collective information extraction" that exploits the mutual in- uence between possible extractions. Experiments on learning to extract protein names from biomedical text demonstrate the advantages of this approach.
