2004 AccurateIEfromResearchPapersUsingCRFs

• (Peng & McCallum, 2004) ⇒ Fuchun Peng, Andrew McCallum. (2004). “Accurate Information Extraction from Research Papers using Conditional Random Fields.” In: Proceedings of the Human Language Technology Conference and North American Chapter of the Association for Computational Linguistics (HLT-NAACL 2004).

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

Notes

• It applies CRFs to extraction from research paper headers and reference sections.
• It obtains state-of-the-art accuracy performance
• It compares some simple regularization methods.
• It was republished as a Journal Paper.
  • (Peng & McCallum, 2006) ⇒ Fuchun Peng, and Andrew McCallum. (2006). “Accurate Information Extraction from Research Papers using Conditional Random Fields.” In: Information Processing & Management, 42(4). doi:10.1016/j.ipm.2005.09.002

Cited By

• ~223 http://scholar.google.com/scholar?q=%22Information+extraction+from+research+papers+using+conditional+random+fields%22

Quotes

Abstract

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.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2004 AccurateIEfromResearchPapersUsingCRFs	Fuchun Peng			Accurate Information Extraction from Research Papers using Conditional Random Fields			http://www.cs.umass.edu/~mccallum/papers/hlt2004.pdf