Classifying Non-Sentential Utterances in Dialogue: A Machine Learning Approach

  title={Classifying Non-Sentential Utterances in Dialogue: A Machine Learning Approach},
  author={Raquel Fern{\'a}ndez and Jonathan Ginzburg and Shalom Lappin},
  journal={Computational Linguistics},
In this article we use well-known machine learning methods to tackle a novel task, namely the classification of non-sentential utterances (NSUs) in dialogue. We introduce a fine-grained taxonomy of NSU classes based on corpus work, and then report on the results of several machine learning experiments. First, we present a pilot study focused on one of the NSU classes in the taxonomybare wh-phrases or sluicesand explore the task of disambiguating between the different readings that sluices can… CONTINUE READING
Highly Cited
This paper has 18 citations. REVIEW CITATIONS

From This Paper

Figures, tables, and topics from this paper.


Publications referenced by this paper.
Showing 1-10 of 27 references

Clarification, Ellipsis, and the Nature of Contextual Updates

Ginzburg, Jonathan, Robin Cooper.
Linguistics and Philosophy, 27(3):297–366. • 2004

Classifying Ellipsis

Shalom Lappin
View 3 Excerpts

The Theory and Use of Clarification Requests in Dialogue

Purver, Matthew.
Ph.D. thesis, King’s College, University of London. • 2004

A Coherence-Based Approach to the Interpretation of Non-Sentential Utterances in Dialogue

Schlangen, David.
Ph.D. thesis, University of Edinburgh, Scotland. • 2003

Maximum Entropy Modeling Toolkit for Python and C++

Le, Zhang. s0450736/maxent_toolkit.html. • 2003

The interpretation of non-sentential utterances in dialogue

Schlangen, David, Alex Lascarides.
Proceedings of the 4th SIGdial Workshop on Discourse and Dialogue, Sapporo, Japan. • 2003

Similar Papers

Loading similar papers…