Latent Dirichlet Allocation

Natural Language Processing

The field of AI focused on enabling computers to understand, interpret, and generate human language.

Seq2Seq Model

A neural network architecture that maps an input sequence to an output sequence, used in translation and summarisation.

Text Embedding

Dense vector representations of text passages that capture semantic meaning for similarity comparison and retrieval.

Semantic Search

Search technology that understands the meaning and intent behind queries rather than just matching keywords.

Vector Database

A database optimised for storing and querying high-dimensional vector embeddings for similarity search.

Constitutional AI

An approach to AI alignment where models are trained to follow a set of principles or constitution.

Natural Language Understanding

The subfield of NLP focused on machine reading comprehension and extracting meaning from text.

Natural Language Generation

The subfield of NLP concerned with producing natural language text from structured data or representations.

Document Understanding

AI systems that extract structured information from unstructured documents by combining optical character recognition, layout analysis, and natural language comprehension.

Slot Filling

The task of extracting specific parameter values from user utterances to fulfil a detected intent, such as identifying dates, locations, and names in booking requests.

Cross-Lingual Transfer

The application of models trained in one language to perform tasks in another language, leveraging shared multilingual representations learned during pre-training.

Text Embedding Model

A neural network trained to convert text passages into fixed-dimensional vectors that capture semantic meaning, enabling similarity search, clustering, and retrieval applications.

The syntactic analysis of a sentence to establish relationships between head words and words that modify them.

An NLP task that identifies and classifies named entities in text into categories like person, organisation, and location.

A summarisation technique that identifies and selects the most important sentences from a source document to compose a condensed version without generating new text.

Identifying semantic relationships between entities mentioned in text.

The process of assigning grammatical categories (noun, verb, adjective) to each word in a text.

The task of identifying all expressions in text that refer to the same real-world entity.

Global Vectors for Word Representation — an unsupervised learning algorithm for obtaining word vector representations from aggregated word co-occurrence statistics.

The process of breaking text into smaller units (tokens) such as words, subwords, or characters for processing by language models.