Character-based language models are statistical models used primarily in natural language processing (NLP), machine learning, and artificial intelligence. These models are based on the assumption that characters are the atomic elements of words and thus can be used to accurately predict the probability for a given word to occur in a language.
Character-based language models are trained on text corpora (a collection of text documents) to learn various languages and their unique features, such as the presence of certain artifacts or words. During training, character-based language models are tuned to determine which combination of characters and other language features is the best combination in order to generate the most accurate predictions.
The primary benefit of character-based language models is that they allow developers to design more sophisticated algorithms that can identify words or phrases in a language, making it easier to build applications that can understand natural language inputs. Additionally, these models can be used to build applications using natural language processing (NLP) and machine learning for tasks such as text categorization and content-based recommendation systems.
However, character-based language models are limited by their reliance on text corpora, which can be difficult to obtain and can limit the accuracy of the model. In addition, the accuracy of character-based language models can be affected by the size and quality of the training data.
Despite these limitations, character-based language models remain a popular model choice among developers for applications that use natural language processing and machine learning. This is due to their ability to accurately detect hidden patterns in text data that can be difficult for more traditional machine learning models to identify.
