Editing Information extraction (section)

==Approaches==
The following standard approaches are now widely accepted:
* Hand-written regular expressions (or nested group of regular expressions)
* Using classifiers
** Generative: [[naïve Bayes classifier]]
** Discriminative: [[Principle of maximum entropy#Maximum entropy models|maximum entropy models]] such as [[Multinomial logistic regression]]
* Sequence models
** [[Recurrent neural network]]
** [[Hidden Markov model]]
** Conditional Markov model (CMM) / [[Maximum-entropy Markov model]] (MEMM)
** [[Conditional random field]]s (CRF) are commonly used in conjunction with IE for tasks as varied as extracting information from research papers<ref>{{Cite journal | doi = 10.1016/j.ipm.2005.09.002 | title = Information extraction from research papers using conditional random fields☆ | year = 2006 | last1 = Peng | first1 = F. | last2 = McCallum | first2 = A. | journal = Information Processing & Management | volume = 42 | issue = 4 | pages = 963}}</ref> to extracting navigation instructions.<ref>{{cite web|title=Extracting Frame-based Knowledge Representation from Route Instructions|last1=Shimizu|first1=Nobuyuki|last2=Hass|first2=Andrew|url=http://www.cs.albany.edu/~shimizu/shimizu+haas2006frame.pdf|year=2006|access-date=2010-03-27|archive-url=https://web.archive.org/web/20060901085639/http://www.cs.albany.edu/~shimizu/shimizu+haas2006frame.pdf|archive-date=2006-09-01|url-status=dead}}</ref>

Numerous other approaches exist for IE including hybrid approaches that combine some of the standard approaches previously listed.