1999 InfExtrWithHMMsAndShrinkage

• (Freitag & McCallum, 1999) ⇒ Dayne Freitag, Andrew McCallum. (1999). “Information Extraction with HMMs and Shrinkage.” In: Proceedings of the AAAI 1999 Workshop on Machine Learning for Information Extraction.

Subject Headings: Relation Recognition from Text Algorithm, Hidden Markov Model

Cited By

~283 http://scholar.google.com/scholar?cites=9120793420316082322

2005

• (Etzioni et al., 2005) ⇒ Oren Etzioni, Michael J. Cafarella, Doug Downey, Ana-Maria Popescu, Tal Shaked, Stephen Soderland, Daniel S. Weld, and Alexander Yates. (2005). “Unsupervised Named-Entity Extraction from the Web: An Experimental Study.” In: Artificial Intelligence, 165(1).
• (Finkel et al., 2005) ⇒ Jenny Rose Finkel, Trond Grenager, and Christopher D. Manning. (2005). “Incorporating Nonlocal Information into Information Extraction Systems by Gibbs Sampling.” In: Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL 2005). doi:10.3115/1219840.1219885.

Quotes

Abstract

• Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling time series data, and have been applied with success to many language-related tasks such as part of speech tagging, speech recognition, text segmentation and topic detection. This paper describes the application of HMMs to another language related task|information extraction|the problem of locating textual sub-segments that answer a particular information need. In our work, the HMM state transition probabilities and word emission probabilities are learned from labeled training data. As in many machine learning problems, however, the lack of suÆcient labeled training data hinders the reliability of the model. The key contribution of this paper is the use of a statistical technique called "shrinkage" that signi cantly improves parameter estimation of the HMM emission probabilities in the face of sparse training data. In experiments on seminar announcements and Reuters acquisitions articles, shrinkage is shown to redure error by up to 40%, and the resulting HMM outperforms a state-of-the-art rule learning system.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1999 InfExtrWithHMMsAndShrinkage	Dayne Freitag			Information Extraction with HMMs and Shrinkage			http://www.cs.umass.edu/~mccallum/papers/ieshrink-aaaiws99.pdf