This position paper identifies a crucial opportunity for the reciprocal exchange of methods, data and phenomena between conversation analysis (CA), ethnomethodology (EM) and computer science (CS). Conventional CS classification of sentiment, tone of voice, or personality do not address what people do with language or the paired sequences that organize actions into social interaction. We argue that CA and EM can innovate and substantially enhance the scope of the dominant CS approaches to big interactional data if artificial intelligence-based natural language processing systems are trained using CA annotated data to do what we call natural action processing.
William Housley, Saul Albert, and Elizabeth Stokoe. 2019. Natural Action Processing: Conversation Analysis and Big Interactional Data. In Proceedings of the Halfway to the Future Symposium 2019 (HTTF 2019), November 19–20, 2019, Nottingham, United Kingdom. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3363384.3363478