Fernandez, R

Ginzburg, J

Lappin, Shalom

English

This paper presents a machine learning approach to bare sluice disambiguation in dialogue. We extract a set of heuristic principles from a corpus-based sample and formulate them as probabilistic Horn clauses. We then use the predicates of such clauses to create a set of domain independent features to annotate an input dataset, and run two different machine learning algorithms: SLIPPER, a rule-based learning algorithm, and TiMBL, a memory-based system. Both learners perform well, yielding similar success rates of approx 90%. The results show that the features in terms of which we formulate our heuristic principles have significant predictive power, and that rules that closely resemble our Horn clauses can be learnt automatically from these features. 

Raquel Fernández

Jonathan Ginzburg

Shalom Lappin

CiteSeerX

Classifying ellipsis in dialogue: a machine learning approach

Fernandez, Raquel

Ginzburg, Jonathan

King's Research Portal

Classifying Ellipsis in Dialogue: A Machine Learning Approach

SAS-SPACE

A Computational Theory of Grounding in Natural Language Conversation.

A simple, fast, and eective rule learner.

Assessing agreement on classi tasks: the kappa statistics.

BNC Database and Word Frequency Lists.

Guess who.

Interrogative Investigations.

Probabilistic representation of formal languages.

SCoRE: A tool for searching the BNC.

Sluicing and logical form. Natural Language Semantics,

The Theory and Use of Clarication

TiMBL: Tilburg Memory Based Learner, Reference Guide.

Using Language.

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.63.8339

Classifying Ellipsis in Dialogue: A Machine Learning Approach

Abstract

Similar works

Full text

Available Versions

CiteSeerX

King's Research Portal

SAS-SPACE