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Context-Sensitive Spoken Dialogue Processing with the DOP Model
1Context-Sensitive Spoken Dialogue Processing
with the DOP Model
Rens Bod
Institute for Logic, Language and Computation
University of Amsterdam
Spuistraat 134, NL-1012 VB Amsterdam
School of Computer Studies
University of Leeds
Leeds LS2 9JT, UK
rens@scs.leeds.ac.uk
Abstract
We show how the DOP model can be used for fast and robust context-sensitive processing of
spoken input in a practical spoken dialogue system called OVIS. OVIS, Openbaar Vervoer
Informatie Systeem (Public Transport Information System), is a Dutch spoken language
information system which operates over ordinary telephone lines. The prototype system is the
immediate goal of the NWO Priority Programme Language and Speech Technology. In this
paper, we extend the original DOP model to context-sensitive interpretation of spoken input. The
system we describe uses the OVIS corpus (which consists of 10,000 trees enriched with
compositional semantics) to compute from an input word-graph the best utterance together with its
meaning. Dialogue context is taken into account by dividing up the OVIS corpus into context-
dependent subcorpora. Each system question triggers a subcorpus by which the user answer is
analyzed and interpreted. Our experiments indicate that the context-sensitive DOP model obtains
better accuracy than the original model, allowing for fast and robust processing of spoken input.
21. Introduction
The Data-Oriented Parsing (DOP) model is a corpus-based parsing model which uses subtrees
from parse trees in a corpus to analyze new sentences. The occurrence-frequencies of the subtrees
are used to estimate the most probable analysis of a sentence (cf. Bod 1992, 93, 95, 98; Bod
Kaplan 1998; Bonnema et al. 1997; Chappelier Rajman 1998; Charniak 1996; Cormons 1999;
Goodman 1996, 98; Kaplan 1996; Rajman 1995; Scha 1990, 92; Scholtes 1992; Sekine
Grishman 1995; Simaan 1995, 97, 99; Tugwell 1995; Way 1999).
To date, DOP has mainly been applied to corpora of trees whose labels consist of primitive
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