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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.
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| 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.
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| Like [[N-gram]] models, smoothing techniques are necessary in parameter estimation. In particular, generalized back-off is used in training an FLM.
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| ==References==
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| *{{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}}
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| [[Category:Statistical natural language processing]]
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| [[Category:Probabilistic models]]
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| {{compu-AI-stub}}
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Latest revision as of 17:20, 10 November 2014
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