Difference between revisions of "Ritter et al NAACL 2010. Unsupervised Modeling of Twitter Conversations"
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| − | == Citation == | + | == Citation == |
Alan Ritter, Colin Cherry, and Bill Dolan. Unsupervised Modeling of Twitter Conversations. In Proc of NAACL 2010 | Alan Ritter, Colin Cherry, and Bill Dolan. Unsupervised Modeling of Twitter Conversations. In Proc of NAACL 2010 | ||
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== Summary == | == Summary == | ||
| − | This [[Category::Paper]] describes a [[UsesMethod::topic model]] based approach | + | This [[Category::Paper]] describes a [[UsesMethod::topic model]] based approach to model dialogue acts. |
| + | Whereas previous work has often required the manual construction of a dialogue act inventory, this paper proposes a series of ''unsupervised'' conversation models, where the discovery of acts amounts to clustering utterances with similar conversational roles. | ||
== Brief description of the method == | == Brief description of the method == | ||
== Experimental Result == | == Experimental Result == | ||
| + | |||
| + | '''Data''': | ||
| + | The corpus consists of about 1.3 million conversations in a 2 month period during the summer of 2009, with each conversation containing between 2 and 243 posts. The dataset was formerly available at http://homes.cs.washington.edu/~aritter/twitter_chat/ (asked by Twitter to be taken down). | ||
== Discussion == | == Discussion == | ||
| − | == Related Papers == | + | == Related Papers == |
| + | The conversation model is inspired by [[RelatedPaper::Barzilay and Lee 2004 Catching the drift: Probabilistic content models, with applications to generation and summarization|Barzilay and Lee. (2004)]] | ||
== Study Plan == | == Study Plan == | ||
Revision as of 17:31, 30 September 2012
Contents
Citation
Alan Ritter, Colin Cherry, and Bill Dolan. Unsupervised Modeling of Twitter Conversations. In Proc of NAACL 2010
Online Version
Unsupervised Modeling of Twitter Conversations.
Summary
This Paper describes a topic model based approach to model dialogue acts. Whereas previous work has often required the manual construction of a dialogue act inventory, this paper proposes a series of unsupervised conversation models, where the discovery of acts amounts to clustering utterances with similar conversational roles.
Brief description of the method
Experimental Result
Data: The corpus consists of about 1.3 million conversations in a 2 month period during the summer of 2009, with each conversation containing between 2 and 243 posts. The dataset was formerly available at http://homes.cs.washington.edu/~aritter/twitter_chat/ (asked by Twitter to be taken down).
Discussion
Related Papers
The conversation model is inspired by Barzilay and Lee. (2004)
