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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. | |||
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}} | |||
Revision as of 11:30, 7 March 2013
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: An FLM provides the probabilistic model where the prediction of a factor is based on parents . For example, if represents a word token and represents a Part of speech tag for English, the expression 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
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