2005 ConditionalRandomFields Wallach

• (Wallach, 2005) ⇒ Hanna M. Wallach. (2005). “Conditional Random Fields.” Literature Survey Webpage.

Subject Headings: CRFs, Literature Survey, Webpage, Unpublished Document.

Cited By

~3316 http://scholar.google.com/scholar?cites=18162940084517866934

Quotes

• Conditional random fields (CRFs) are a probabilistic framework for labeling and segmenting structured data, such as sequences, trees and lattices. The underlying idea is that of defining a conditional probability distribution over label sequences given a particular observation sequence, rather than a joint distribution over both label and observation sequences. The primary advantage of CRFs over hidden Markov models is their conditional nature, resulting in the relaxation of the independence assumptions required by HMMs in order to ensure tractable inference. Additionally, CRFs avoid the label bias problem, a weakness exhibited by maximum entropy Markov models (MEMMs) and other conditional Markov models based on directed graphical models. CRFs outperform both MEMMs and HMMs on a number of real-world tasks in many fields, including bioinformatics, computational linguistics and speech recognition.

Refences

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2005 ConditionalRandomFields Wallach	Hanna M. Wallach			Conditional Random Fields			http://www.inference.phy.cam.ac.uk/hmw26/crf/			2005