LanguageModellierung

Language modeling refers to the process of creating statistical models that can predict the probability of a sequence of words occurring. These models are fundamental to many natural language processing (NLP) tasks. At its core, a language model learns the patterns and structures of a given language from a large corpus of text data. It assigns a probability to a given sequence of words, essentially indicating how likely that sequence is to be found in the language.

The simplest form of language modeling is n-gram modeling, where the probability of a word is conditioned on the preceding n-1 words. For example, a bigram model considers only the previous word, while a trigram model considers the two preceding words. While effective for certain tasks, n-gram models struggle with capturing long-range dependencies in text.

More advanced language models utilize neural networks, particularly recurrent neural networks (RNNs) and more recently, transformer architectures. These models can learn more complex relationships between words and capture longer contexts. Neural language models have significantly improved performance on tasks like machine translation, text generation, and speech recognition. The training process involves feeding the model vast amounts of text, allowing it to learn word embeddings and contextual relationships. The output of a language model can be used to generate text that is grammatically correct and contextually relevant, or to evaluate the fluency of existing text.