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Probabilistic framework for labeling and segmenting sequential data, based on context.

Based on self-attention mechanisms, these models are state-of-the-art for various NLP tasks, including language identification.

Representation of text in an n-dimensional space. Word2Vec and GloVe are common algorithms for generating embeddings.

Supervised learning model that finds the hyperplane that best separates data points of different classes.

Statistical method based on character frequencies. Common algorithms include Markov Chains and N-gram language models.

Model that splits the data based on feature values, forming a tree structure.

Probabilistic model that applies Bayes' Theorem. It's a simple and effective baseline for language identification.

Statistical model in which the system being modeled is assumed to be a Markov process with hidden states.

Uses hand-crafted rules for decision-making based on linguistic knowledge, often with the help of dictionaries and language rules.