1998/06/02 by Ken Samuel, Sandra Carberry, K. Vijay-Shanker
Computer Science · #cmp-lg #cs.CL
published as Applying Machine Learning to Discourse Processing: Papers from the 1998 AAAI Spring Symposium · 8 pages, 1 Postscript figure, uses aaai.sty and aaai.bst
arxiv created 1998/06/02 · arxiv updated 2009/11/30
To interpret natural language at the discourse level, it is very useful to accurately recognize dialogue acts, such as SUGGEST, in identifying speaker intentions. Our research explores the utility of a machine learning method called Transformation-Based Learning (TBL) in computing dialogue acts, because TBL has a number of advantages over alternative approaches for this application. We have identified some extensions to TBL that are necessary in order to address the limitations of the original algorithm and the particular demands of discourse processing. We use a Monte Carlo strategy to increase the applicability of the TBL method, and we select features of utterances that can be used as input to improve the performance of TBL. Our system is currently being tested on the VerbMobil corpora of spoken dialogues, producing promising preliminary results.