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

TY - JOUR

T1 - Classifying Non-Sentential Utterances in Dialogue

T2 - A Machine Learning Approach

AU - Fernandez, Raquel

AU - Ginzburg, Jonathan

AU - Lappin, Shalom

PY - 2007/9

Y1 - 2007/9

N2 - 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 taxonomy—bare wh-phrases or “sluices”—and explore the task of disambiguating between the different readings that sluices can convey. We then extend the approach to classify the full range of NSU classes, obtaining results of around an 87% weighted F-score. Thus our experiments show that, for the taxonomy adopted, the task of identifying the right NSU class can be successfully learned, and hence provide a very encouraging basis for the more general enterprise of fully processing NSUs.

AB - 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 taxonomy—bare wh-phrases or “sluices”—and explore the task of disambiguating between the different readings that sluices can convey. We then extend the approach to classify the full range of NSU classes, obtaining results of around an 87% weighted F-score. Thus our experiments show that, for the taxonomy adopted, the task of identifying the right NSU class can be successfully learned, and hence provide a very encouraging basis for the more general enterprise of fully processing NSUs.

U2 - 10.1162/coli.2007.33.3.397

DO - 10.1162/coli.2007.33.3.397

M3 - Article

VL - 33

SP - 397

EP - 427

JO - COMPUTATIONAL LINGUISTICS

JF - COMPUTATIONAL LINGUISTICS

IS - 3

ER -