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Definition: A simple probabilistic classifier based on applying Bayes’ theorem with strong (naive) independence assumptions between the features. It is particularly suited for very large datasets.

Definition: A field of AI that focuses on the interaction between computers and humans through natural language. The ultimate objective of NLP is to read, decipher, understand, and make sense of human languages in a manner that is valuable.

Definition: A sub-discipline of NLP that focuses specifically on machine reading comprehension, encompassing tasks such as mapping the given input in natural language into useful representations.

Definition: An approach to data classification that involves finding the closest data points in the training dataset to the new data point and predicting based on their classifications.

Definition: A training technique used to simplify the learning process in large datasets by teaching a model what to ignore. This is especially useful in training neural networks on word predictions for handling large vocabularies efficiently.

Definition: An area of machine learning which focuses on automating the design of artificial neural networks. It dynamically selects a neural network architecture that can best perform a specific task.

Definition: A series of algorithms that attempt to recognise underlying relationships in a set of data through a process that mimics the way the human brain operates. Neural networks can adapt to changing input; hence, the network generates the best possible result without needing to redesign the output criteria.

Definition: A pseudoscientific approach to communication, personal development, and psychotherapy created in the 1970s. Not directly related to artificial intelligence but shares an acronym with Natural Language Processing.

Definition: A form of artificial intelligence that uses evolutionary algorithms to generate artificial neural networks, topology, and weights.

Definition: In the context of neural networks, a node is a single processing element of a network, which receives input and performs a simple computation on the input. In the context of graph theory, a node is a point at which lines or pathways intersect or branch.

Definition: A process in data preprocessing used to change the values of numeric columns in a dataset to a common scale, without distorting differences in the ranges of values or losing information. It is often required by machine learning algorithms to ensure accurate results.

Definition: A contiguous sequence of n items from a given sample of text or speech. The items can be phonemes, syllables, letters, words, or base pairs according to the application. N-grams are used in various applications in statistical natural language processing and genetic sequence analysis.

Definition: Data with a large amount of additional meaningless information in it, making it difficult to accurately assess or use. Cleaning noisy data is an important step before it can be used effectively for training models.

Definition: Statistical models that do not assume a fixed structure or form for the model but rather make fewer assumptions about the data, thereby allowing the model to dynamically adapt to the complexity of the data.

Definition: A form of regression analysis in which observational data is modelled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables. Nonlinear regression is used to model complex relationships between variables where linear models are inadequate.