Factored Language Model
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Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. 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: w_i = {f_i^1, ..., f_i^k}. An FLM provides the probabilistic model P(f f1,...,fN) where the prediction of a factor f is based on N parents {f1,...,fN}. For example, if w represents a word token and t represents a Part of speech tag for English, the expression P(wi wi 2,wi 1,ti 1) gives a model for predicting current word token ba...
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. 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: w_i = {f_i^1, ..., f_i^k}. An FLM provides the probabilistic model P(f f1,...,fN) where the prediction of a factor f is based on N parents {f1,...,fN}. For example, if w represents a word token and t represents a Part of speech tag for English, the expression P(wi wi 2,wi 1,ti 1) 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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