Natural Language Processing
Language Model

Language Model

A language model is a type of Artificial Intelligence model that is designed to predict the probability of a sequence of words in a given language. Language models are commonly used in Natural Language Processing tasks such as speech recognition, machine translation, and text generation.

A language model is trained on a large corpus of text, such as books, articles, or transcripts of speech. The model is trained to predict the next word in a sequence of words given the previous words in the sequence. The goal is to learn the probability distribution of words in the language, so that the model can generate fluent and coherent sentences.

There are two main types of language models: n-gram models and Neural Network-based models. N-gram models are based on the frequency of sequences of n words in the training corpus, while Neural Network-based models use Deep Learning techniques such as recurrent Neural Networks (RNNs) and transformers to learn the probability distribution of words in the language.

Language models are a key component of many Natural Language Processing systems, such as speech recognition systems, machine translation systems, and chatbots. By predicting the probability of words in a given language, language models enable intelligent systems to understand and generate natural language text, making them a crucial tool for a wide range of applications.