28 citations · 91 across the 5 of their papers we have counts for
5 papers
Automatically Selecting Useful Phrases for Dialogue Act Tagging
Ken Samuel, Sandra Carberry, K. Vijay-Shanker
We present an empirical investigation of various ways to automatically identify phrases in a tagged corpus that are useful for dialogue act tagging. We found that a new method (whi…
An Investigation of Transformation-Based Learning in Discourse
Ken Samuel, Sandra Carberry, K. Vijay-Shanker
This paper presents results from the first attempt to apply Transformation-Based Learning to a discourse-level Natural Language Processing task. To address two limitations of the s…
Dialogue Act Tagging with Transformation-Based Learning
Ken Samuel, Sandra Carberry, K. Vijay-Shanker
For the task of recognizing dialogue acts, we are applying the Transformation-Based Learning (TBL) machine learning algorithm. To circumvent a sparse data problem, we extract value…
Lazy Transformation-Based Learning
Ken Samuel
We introduce a significant improvement for a relatively new machine learning method called Transformation-Based Learning. By applying a Monte Carlo strategy to randomly sample from…
Computing Dialogue Acts from Features with Transformation-Based Learning
Ken Samuel, Sandra Carberry, K. Vijay-Shanker
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 expl…