Notes:
Language modeling is a task in natural language processing (NLP) that involves predicting the likelihood of a sequence of words in a language. It is commonly used in a variety of applications, such as speech recognition, machine translation, and text generation.
In a language model, a sequence of words is considered to be a sample from a larger population of possible sequences. The goal of language modeling is to estimate the probability of a particular sequence of words occurring in the population, given some context. This probability can be used to predict the likelihood of a given sequence of words in a given context, or to compare the likelihood of different sequences of words.
Language models can be based on a variety of techniques, such as statistical language models, which estimate the probability of a sequence of words based on their frequency of occurrence in a large corpus of text, or neural language models, which use machine learning techniques to estimate the probability of a sequence of words based on their relationships to other words in the sequence.
Language modeling is an important task in NLP because it enables systems to understand and generate natural language text, and to make decisions about which actions to take in response to a given input. It is a key component of many NLP systems, and has a wide range of applications in areas such as speech recognition, machine translation, and text generation.
Wikipedia:
See also:
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