Publication Date
2004
Abstract
Although information extraction and coref- erence resolution appear together in many applications, most current systems perform them as independent steps. This paper describes an approach to integrated infer- ence for extraction and coreference based on conditionally-trained undirected graphical models. We discuss the advantages of condi- tional probability training, and of a corefer- ence model structure based on graph parti- tioning. On a data set of research paper cita- tions, 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.
Recommended Citation
Wellner, Ben; McCallum, Andrew; Peng, Fuchun; and Hay, Michael, "An Integrated, Conditional Model of Information Extraction and Coreference with Application to Citation Matching" (2004). Computer Science Department Faculty Publication Series. 24.
Retrieved from https://scholarworks.umass.edu/cs_faculty_pubs/24
Comments
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