Natural action processing conversation analysis and big interactional data
conference contribution
posted on 2020-01-07, 09:47 authored by W Housley, Saul AlbertSaul Albert, Elizabeth Stokoe© 2019 Copyright held by the owner/author(s). 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.
History
School
- Social Sciences
Published in
ACM International Conference Proceeding SeriesPages
34Source
HTTF 2019: Proceedings of the Halfway to the Future Symposium 2019Publisher
Association for Computing Machinery (ACM)Version
- AM (Accepted Manuscript)
Rights holder
© The AuthorsPublisher statement
© Author 2019. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in HTTF 2019: Proceedings of the Halfway to the Future Symposium 2019, https://doi.org/10.1145/3363384.3363478Publication date
2019-11-19ISBN
9781450372039Publisher version
Language
- en
Editor(s)
Joel E Fischer; Sarah MartindaleLocation
NottinghamEvent dates
Nov 19-20Depositor
Dr Saul Albert Deposit date: 3 January 2020Usage metrics
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