Natural Language Processing

Seq2Seq Model

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

Latent Dirichlet Allocation

A generative probabilistic model for discovering topics in a collection of documents.

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

A subword tokenisation algorithm that iteratively merges the most frequent character pairs to build a vocabulary.

A text summarisation approach that generates novel sentences to capture the essential meaning of a document, rather than simply extracting and rearranging existing sentences.

The process of automatically extracting structured information from unstructured or semi-structured text sources.

The task of assigning predefined categories or labels to text documents based on their content.

The computational study of people's opinions, emotions, and attitudes expressed in text.

A text generation strategy that restricts the model to sampling from the K most probable next tokens.

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