1995 TrasformationBasedErrorDrivenPOSTagging

• (Brill, 1995) ⇒ Eric D. Brill. (1995). “Transformation-based Error-Driven Learning and Natural Language Processing: A Case Study in Part of Speech Tagging.” In: Computational Linguistics, 21(4).

Subject Headings: Part-of-Speech Tagging Algorithm, Transformation-based Learning Algorithm.

Notes

• ACM Digital Library page http://portal.acm.org/citation.cfm?id=218367

Cited By

~1288 papers http://scholar.google.com/scholar?cites=6669039697562179777

2003

• (Sha & Pereira, 2003a) ⇒ Fei Sha, and Fernando Pereira. (2003). “Shallow Parsing with Conditional Random Fields.” In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology (HLT-NAACL 2003). doi:10.3115/1073445.1073473
• (Collins, 2003) ⇒ Michael Collins. (2003). “Head-Driven Statistical Models for Natural Language Parsing.” In: Computational Linguistics, 29(4). doi:10.1162/089120103322753356.

2001

• (Lafferty et al., 2001) ⇒ John D. Lafferty, Andrew McCallum, and Fernando Pereira. (2001). “Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data.” In: Proceedings of ICML 2001.

2000

• Andrew McCallum, Dayne Freitag, Fernando Pereira. (2000). “Maximum entropy Markov models for information extraction and segmentation.” In: Proceedings17th International Conference on Machine Learning (ICML 2000).

Quotes

Abstract

• Recently, there has been a rebirth of empiricism in the field of natural language processing. Manual encoding of linguistic information is being challenged by automated corpus-based learning as a method of providing a natural language processing system with linguistic knowledge. Although corpus-based approaches have been successful in many different areas of natural language processing, it is often the case that these methods capture the linguistic information they are modelling indirectly in large opaque tables of statistics. This can make it difficult to analyze, understand and improve the ability of these approaches to model underlying linguistic behavior. In this paper, we will describe a simple rule-based approach to automated learning of linguistic knowledge. This approach has been shown for a number of tasks to capture information in a clearer and more direct fashion without a compromise in performance. We present a detailed case study of this learning method applied to part-of-speech tagging.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1995 TrasformationBasedErrorDrivenPOSTagging	Eric D. Brill			Transformation-based Error-Driven Learning and Natural Language Processing: A Case Study in Part of Speech Tagging		Computational Linguistics (CL) Research Area	http://www.cs.mu.oz.au/acl/J/J95/J95-4004.pdf			1995