Factored language model: Difference between revisions

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The '''factored language model''' ('''FLM''') is an extension of a conventional [[language model]] introduced by Jeff Bilmes and Katrin Kirchoff in 2003. 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_if_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.

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==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 | booktitlebook-title=Human Language Technology Conference | pagesyear=2003 | yeararchive-url=2003https://web.archive.org/web/20120717075838/http://ssli.ee.washington.edu/people/bilmes/mypapers/hlt03.pdf | archive-date=17 July 2012}}

[[Category:Language modeling]]

[[Category:Statistical natural language processing]]

[[Category:Probabilistic models]]

{{compu-AINLP-stub}}