|Abstract||We propose a method for automatically identifying rhetorical relations. We use supervised machine learning but exploit cue phrases to automatically extract and label training data. Our models draw on a variety of linguistic cues to distinguish between the relations. We show that these feature-rich models outperform the previously suggested bigram models by more than 20%, at least for small training sets. Our approach is therefore better suited to deal with relations for which it is difficult to automatically label a lot of training data because they are rarely signalled by unambiguous cue phrases (e.g., Continuation).|
|Keywords||No keywords specified (fix it)|
|Categories||No categories specified (fix it)|
|Through your library||Only published papers are available at libraries|
Similar books and articles
Joop Leo (2008). Modeling Relations. Journal of Philosophical Logic 37 (4).
Keith S. Apfelbaum & Bob McMurray (2011). Using Variability to Guide Dimensional Weighting: Associative Mechanisms in Early Word Learning. Cognitive Science 35 (6):1105-1138.
Dominic W. Massaro (1998). Integrating Cues in Speech Perception. Behavioral and Brain Sciences 21 (2):275-275.
Ioannis Votsis (2011). Data Meet Theory: Up Close and Inferentially Personal. Synthese 182 (1):89-100.
Brian Riordan & Michael N. Jones (2011). Redundancy in Perceptual and Linguistic Experience: Comparing Feature-Based and Distributional Models of Semantic Representation. Topics in Cognitive Science 3 (2):303-345.
Nikiforos Karamanis (2007). Supplementing Entity Coherence with Local Rhetorical Relations for Information Ordering. Journal of Logic, Language and Information 16 (4).
Shannon A. Bowen (2010). An Overview of the Public Relations Function. Business Expert Press.
Calvin L. Troup (2009). Ordinary People Can Reason: A Rhetorical Case for Including Vernacular Voices in Ethical Public Relations Practice. Journal of Business Ethics 87 (4):441 - 453.
M. Saravanan & B. Ravindran (2010). Identification of Rhetorical Roles for Segmentation and Summarization of a Legal Judgment. Artificial Intelligence and Law 18 (1):45-76.
Added to index2009-01-28
Total downloads2 ( #232,684 of 549,699 )
Recent downloads (6 months)1 ( #63,425 of 549,699 )
How can I increase my downloads?