## What are the drawbacks of an n-gram language model ?

n-gram language model is a non deep learning method to generate language model. Probability of a word, w, (after sequence of 2 words) for a 2 gram model is given by – P(w | “word_1 word_2”) = count( “word_1 word_2 w”) / count( “word_1 word_2” ) , where “word_1 word_2” is ordered sequence of two…

## What order of Markov assumption does n-grams model make ?

An n-grams model makes order n-1 Markov assumption. This assumption implies: given the previous n-1 words, probability of  word is independent of words prior to words. Suppose we have k words in a sentence, their joint probability can be expressed as follows using chain rule:      Now, the Markov assumption can be used to make…

## How is long term dependency maintained while building a language model?

Language models can be built using the following popular methods – Using n-gram language model n-gram language models make assumption for the value of n. Larger the value of n, longer the dependency. One can refer to what is the significance of n-grams in a language model for further reading. Using hidden Markov Model(HMM) HMM maintains long…

## What is the significance of n-grams in a language model ?

n-grams is a term used for a sequence of n consecutive words/tokens/grams. In general, n-grams can either preserve the ordering or indicate what level of dependency is required in order to simplify the modeling task. While using bag of Words, n-grams come handy to preserve ordering between words but for language modeling, they signify the…

## If the average length of a sentence is 100 in all documents, should we build 100-gram language model ?

A 100 gram model will be more complex and will have lot of parameters. One way is to start with n-gram model with different values of n from 2 to 10 worst case. After some value of n, say n=7, the accuracy of the model becomes almost stagnant. One reason for this could be that…