2004 AccurateIEfromResearchPapersUsingCRFs

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Subject Headings: Information Extraction Task, Linear-Chain CRF, CRF Training Algorithm, Regularization, Citation Information Extraction Task, Constraint Information Extraction.


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With the increasing use of research paper search engines, such as CiteSeer, for both literature search and hiring decisions, the accuracy of such systems is of paramount importance. This article employs conditional random fields (CRFs) for the task of extracting various common fields from the headers and citation of research papers. CRFs provide a principled way for incorporating various local features, external lexicon features and global layout features. The basic theory of CRFs is becoming well-understood, but best-practices for applying them to real-world data requires additional exploration. We make an empirical exploration of several factors, including variations on Gaussian, Laplace and hyperbolic-L1 priors for improved regularization, and several classes of features. Based on CRFs, we further present a novel approach for constraint co-reference information extraction; i.e., improving extraction performance given that we know some citations refer to the same publication. On a standard benchmark dataset, we achieve new state-of-the-art performance, reducing error in average F1 by 36%, and word error rate by 78% in comparison with the previous best SVM results. Accuracy compares even more favorably against HMMs.


 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2004 AccurateIEfromResearchPapersUsingCRFsFuchun Peng
Andrew McCallum
Accurate Information Extraction from Research Papers using Conditional Random FieldsProceedings of the Human Language Technology Conference and North American Chapter of the Association for Computational Linguisticshttp://www.cs.umass.edu/~mccallum/papers/hlt2004.pdf2004