E2F - Revision history

en>Austinprince: /* E2F/pRb complexes */ Just added 'that' to make the sentence grammatical.

2014-11-10T15:20:50Z

E2F/pRb complexes: Just added 'that' to make the sentence grammatical.

← Older revision		Revision as of 17:20, 10 November 2014
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	~~The '''factored language model''' ('''FLM''') is an extension of a conventional [[language model]].~~ ~~In an FLM~~, ~~each word is viewed as a vector of ''k'' factors: <math>w_i = \{f_i^1~~, .~~.., f_i^k\}.</math>~~ ~~An FLM provides~~ the ~~probabilistic model <math>P(f\|f_1,~~ ..., f_N)</math> where the prediction of a factor <math>f</math> is based on <math>N</math> parents <math>\{f_1, ..., f_N\}</math>. For example, if <math>w</math> represents a word token and <math>t</math> represents a [[Part of speech]] tag for English, the expression <~~math~~>~~P(w_i\|w_{i-2}, w_{i-1}, t_{i-1})~~<~~/math~~> ~~gives a model for predicting current word token based on a traditional [[Ngram]] model as well as the [[Part of speech]] tag of the previous word.~~		Air Move Specialists Cadotte from Clarence Creek, spends time with interests for example house brewing, new launch property singapore and toy collecting. Remembers what a beautiful area it was having paid checking out the Mount Wuyi.<br><br>my webpage new launch condo singapore ([http://truyen.meximas.com/index.php?do=/profile-14268/info/ Get More Information])

	A major advantage of factored language models is that they allow users to specify linguistic knowledge such as the relationship between word tokens and [[Part of speech]] in English, or morphological information (~~stems, root, etc.) in Arabic.~~

	~~Like~~ [~~[N-gram]] models, smoothing techniques are necessary in parameter estimation. In particular, generalized back-off is used in training an FLM.~~

	~~==References==~~
	*{{cite conference \| author=J Bilmes and K Kirchhoff \| url=http://~~ssli~~.ee.~~washington~~.~~edu~~/~~people/bilmes~~/~~mypapers~~/~~hlt03.pdf \| title=Factored Language Models and Generalized Parallel Backoff \| booktitle=Human Language Technology Conference \| pages= \| year=2003}}~~

	~~[[Category:Statistical natural language processing~~]]
	~~[[Category:Probabilistic models]]~~

	~~{{compu-AI-stub}}~~

en>Addbot: Bot: Migrating 3 interwiki links, now provided by Wikidata on d:q1114187

2013-03-07T09:30:59Z

Bot: Migrating 3 interwiki links, now provided by Wikidata on d:q1114187

← Older revision		Revision as of 11:30, 7 March 2013
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	I'~~m Bridgette and I live in~~ a ~~seaside city in northern United States~~, ~~Westlake. I~~'~~m 27 and I~~'~~m will soon finish my study at American Politics~~.<br><br>~~My website~~ - [http://~~Localparanormal~~.~~com~~/~~members~~/~~bradkerrigan~~/~~activity~~/~~2076/ fifa Coin generator~~]		The '''factored language model''' ('''FLM''') is an extension of a conventional [[language model]]. In an FLM, each word is viewed as a vector of ''k'' factors: <math>w_i = \{f_i^1, ..., f_i^k\}.</math> An FLM provides the probabilistic model <math>P(f\|f_1, ..., f_N)</math> where the prediction of a factor <math>f</math> is based on <math>N</math> parents <math>\{f_1, ..., f_N\}</math>. For example, if <math>w</math> represents a word token and <math>t</math> represents a [[Part of speech]] tag for English, the expression <math>P(w_i\|w_{i-2}, w_{i-1}, t_{i-1})</math> gives a model for predicting current word token based on a traditional [[Ngram]] model as well as the [[Part of speech]] tag of the previous word.

			A major advantage of factored language models is that they allow users to specify linguistic knowledge such as the relationship between word tokens and [[Part of speech]] in English, or morphological information (stems, root, etc.) in Arabic.

			Like [[N-gram]] models, smoothing techniques are necessary in parameter estimation. In particular, generalized back-off is used in training an FLM.

			==References==
			*{{cite conference \| author=J Bilmes and K Kirchhoff \| url=http://ssli.ee.washington.edu/people/bilmes/mypapers/hlt03.pdf \| title=Factored Language Models and Generalized Parallel Backoff \| booktitle=Human Language Technology Conference \| pages= \| year=2003}}

			[[Category:Statistical natural language processing]]
			[[Category:Probabilistic models]]

			{{compu-AI-stub}}

en>Zorrobot: r2.5.2) (Robot: Adding sv:E2F

2012-05-12T13:45:24Z

r2.5.2) (Robot: Adding sv:E2F

New page

I'm Bridgette and I live in a seaside city in northern United States, Westlake. I'm 27 and I'm will soon finish my study at American Politics.<br><br>My website - [http://Localparanormal.com/members/bradkerrigan/activity/2076/ fifa Coin generator]