Text Summarisation

Sentiment Analysis

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

Text Classification

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

Topic Modelling

An unsupervised technique for discovering abstract topics that occur in a collection of documents.

Abstractive Summarisation

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

Aspect-Based Sentiment Analysis

A fine-grained sentiment analysis approach that identifies opinions directed at specific aspects or features of an entity, such as a product's price, quality, or design.

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

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

The generation of machine-readable formatted responses such as JSON, XML, or code from language models, enabling reliable integration with downstream software systems.

Generative Pre-trained Transformer — a family of autoregressive language models that generate text by predicting the next token.

An NLP task where a system automatically answers questions posed in natural language based on given context.

Technology that converts written text into natural-sounding spoken audio using neural networks, enabling voice interfaces, accessibility tools, and content narration.

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

Techniques and architectures that enable language models to process and reason over extremely long input sequences, from tens of thousands to millions of tokens.