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Language Identification Methods
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Hidden Markov Models (HMM)
Statistical model in which the system being modeled is assumed to be a Markov process with hidden states.
Naive Bayes Classifier
Probabilistic model that applies Bayes' Theorem. It's a simple and effective baseline for language identification.
N-gram Models
Statistical method based on character frequencies. Common algorithms include Markov Chains and N-gram language models.
Support Vector Machines (SVM)
Supervised learning model that finds the hyperplane that best separates data points of different classes.
Transformer Models
Based on self-attention mechanisms, these models are state-of-the-art for various NLP tasks, including language identification.
Decision Trees
Model that splits the data based on feature values, forming a tree structure.
Neural Networks
Biologically-inspired programming paradigm which enables a computer to learn from observational data.
Rule-Based Systems
Uses hand-crafted rules for decision-making based on linguistic knowledge, often with the help of dictionaries and language rules.
Conditional Random Fields (CRF)
Probabilistic framework for labeling and segmenting sequential data, based on context.
Text Embeddings
Representation of text in an n-dimensional space. Word2Vec and GloVe are common algorithms for generating embeddings.
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