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Natural Language Processing, the branch of computer science and artificial intelligence concerned with giving computers the ability to understand text and spoken words in much the same way human beings can.

The process of computationally determining whether a piece of writing is positive, negative, or neutral.

The process of breaking down text into units of meaning, known as tokens, which can be words, phrases, or symbols.

Term Frequency-Inverse Document Frequency, a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus.

A component of neural networks that allows the model to focus on different parts of the input sequence when generating an output, improving the performance of the network on tasks like machine translation.

A type of word representation that allows words with similar meaning to have a similar representation.

A large and structured set of texts, typically used for linguistic research and language modeling.

A type of deep learning model that uses self-attention to boost the speed of training and improve the handling of long-range dependencies in data.

The task of determining whether and how terms in a text, such as pronouns and nouns, refer to the same entity.

The process of analyzing a string of symbols, either in natural language or in computer languages, according to the rules of a formal grammar.

The process of marking up words in a text as corresponding to a particular part of speech, based on both its definition and context.

The arrangement of words and phrases to create well-formed sentences in a language.

The process of identifying and classifying named entities in text into predefined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc.

A model that predicts the likelihood of a sequence of words.

In NLP, a token usually corresponds to a word or a punctuation marker.

A word or phrase that clearly identifies one item from a set of others that have similar attributes, such as names of people, places, companies, etc.

The process of reducing words to their root form or stem, often by removing common prefixes or suffixes.

Bidirectional Long Short-Term Memory, a type of RNN that can improve model performance on sequence classification problems.

Words that are filtered out before processing of natural language data because they are deemed irrelevant for the task and do not contain important meaning.

Bidirectional Encoder Representations from Transformers, a method of pre-training language representations which can be fine-tuned for various NLP tasks.

Similar to stemming, it's the process of reducing a word to its base or dictionary form, called a lemma, often involving a vocabulary and morphological analysis of the word.

The application of computers to the task of translating texts from one natural language to another.

Generative Pre-trained Transformer, a type of language model that uses unsupervised learning to generate natural language texts.

The study of meaning in language, as opposed to syntax which is the study of sentence structure.