An Integrated, Conditional Model of Information Extraction and Coreference with Applications to Citation Matching
Abstract
Although information extraction and coreference resolution appear together in many applications, most current systems perform them as ndependent steps. This paper describes an approach to integrated inference for extraction and coreference based on conditionally-trained undirected graphical models. We discuss the advantages of conditional probability training, and of a coreference model structure based on graph partitioning. On a data set of research paper citations, we show significant reduction in error by using extraction uncertainty to improve coreference citation matching accuracy, and using coreference to improve the accuracy of the extracted fields.
Keywords
Cite
@article{arxiv.1207.4157,
title = {An Integrated, Conditional Model of Information Extraction and Coreference with Applications to Citation Matching},
author = {Ben Wellner and Andrew McCallum and Fuchun Peng and Michael Hay},
journal= {arXiv preprint arXiv:1207.4157},
year = {2012}
}
Comments
Appears in Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence (UAI2004)