Understanding the structure of conversation online

This project is funded by a grant from the Alan Turing Institute.

The overarching goal of this research programme is to understand the structure of conversation at scale by drawing on the increasingly large amounts of conversation data available online. By combining methods from corpus linguistics, discourse analysis, and conversation structure, with advanced techniques from data science, natural language processing, and machine learning, we describe and model the range of conversational turns and the relationships between conversational turns found in natural interactions, with the ultimate aim of building a grammar of conversation. In addition, we are interested in applying insights from this research to the automatic processing of online conversation

External resources:

Project page on the Alan Turing Institute website

Project team:

• Jack Grieve (ELAL) - PI
• Emily Chiang (Aston University) - Research Fellow, 2018-19
• Dong Nguyen (Utrecht University) - Co-I, 2019-19
• Amanda Towler (Hyperion Gray) - External Partner, 2018-19
• Martine Van Driel (ELAL) - Research Fellow, 2020-21