In language modeling, n-gram models are probabilistic models of text that use some limited amount of history, or word dependencies, where n refers to the number of words that participate in the dependence relation.
In automatic speech recognition, n-grams are important to model some of the structural usage of natural language, i.e., the model uses word dependencies to assign a higher probability to “how are you today” than to “are how today you,” although both phrases contain the exact same words. If used in information retrieval, simple unigram language models ( n-gram models with n = 1), i.e., models that do not use term dependencies, result in good quality retrieval in many studies. The use of bigram models ( n-gram models with n= 2) would allow the system to model direct term dependencies, and treat the occurrence of “New York” differently from separate occurrences of “New” and “York,” possibly improving retrieval performance. The use of trigram models would...
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