Interaction Mining

Supervisor: Professor Matthew Purver

Research group(s): Cognitive Science

With the large datasets of human interaction we have available today, we often want to find and summarise specific meaningful events: decisions made in business meetings, answers to questions in radio broadcasts, agreements and disagreements in social media. This is a challenging problem: rather than identifying keywords or specific sentence structures categories, we must identify structural patterns in the interaction itself (e.g. the characteristic conversational patterns of the decision-making process). The events in question are also often rare, and often involve more than two people: structures are consequently more complex, less predictable, and less well suited to common dialogue models. This project will develop methods which combine rule-based models of dialogue structure with robust statistical approaches to solve this problem, exploiting recent advances in NLP and machine learning such as distributional models of meaning and deep learning.