Modeling Conversations in Social Media


September 28, 2011


Alan Ritter


University of Washington


Users of social media websites are engaging in public conversations at an unprecedented scale. This massive corpus of naturally occurring, open-domain conversations presents new challenges and opportunities for data-driven conversation modeling. In this talk I will describe work on automatically generating replies to open-domain Status Messages in Twitter. In order to leverage millions of naturally occurring conversations, we adapt techniques from Statistical Machine Translation (SMT), to build a models capable of “translating” an arbitrary status message into an appropriate response. Open-domain response generation could be useful for several applications including language generation in dialogue systems, and conversationally aware predictive text input.
In addition I will describe work on unsupervised induction of dialogue acts in Twitter. By remaining agnostic about the set of dialogue acts, we are able to learn a model which provides insight into the nature of communication in a new medium.


Alan Ritter

Alan Ritter is a graduate student at the University of Washington advised by Oren Etzioni. His research interests are in Language Processing in Social Media, Information Extraction, Computational Lexical Semantics.