The use of probability theory and statistics to model language and language data, including techniques such as machine learning and natural language processing.
Probability theory: This is the foundation of statistical models, which deals with the chances of events occurring.
Descriptive statistics: This involves the use of graphical and numerical methods to summarize and describe data.
Inferential statistics: This is the process of drawing conclusions or making predictions about a population based on sample data.
Regression analysis: This is a statistical modeling technique that examines the relationship between a dependent variable and one or more independent variables.
Bayesian statistics: This is a statistical approach that combines prior knowledge with current evidence to make predictions or draw conclusions about uncertain events.
Machine learning algorithms: These are algorithms that enable computers to learn from data without being explicitly programmed.
Natural language processing: This is a subfield of computational linguistics that focuses on the interaction between computers and human languages.
Text mining: This is the process of extracting useful information or knowledge from unstructured text data.
Data visualization: This is the graphical representation of data, which helps to identify patterns, trends, and insights from large datasets.
Sentiment analysis: This is a form of text analysis that involves identifying and extracting opinions, emotions, and attitudes from text data.
Hidden Markov Models (HMMs): Used to model sequential data where the underlying sequence of states is unknown.
Conditional Random Fields (CRFs): A type of discriminative probabilistic model used for labeling and parsing sequential data.
Latent Dirichlet Allocation (LDA): A probabilistic topic model used for discovering hidden topics in large collections of text.
Maximum Entropy Markov Models (MEMMs): A statistical model used for sequence labeling problems, where a label is assigned to each element in the sequence.
Neural Networks: A machine learning algorithm that can be used for a wide range of tasks, including text classification, image classification, and sequence prediction.
Support Vector Machines (SVMs): A supervised machine learning algorithm used for classification and regression analysis.
Bayesian Networks: A probabilistic graphical model that represents a set of random variables and their conditional dependencies.
Linear Regression: A statistical model used to analyze the relationship between two or more variables.
Naive Bayes: A probabilistic machine learning algorithm used for text classification and spam filtering.
Markov Random Fields (MRFs): A probabilistic model used for modeling spatial or spatiotemporal data.