[Colloquium] Tomorrow: Boven/MS Presentation/March 8, 2007
Margaret Jaffey
margaret at cs.uchicago.edu
Wed Mar 7 13:35:12 CST 2007
This is a reminder about Brad Boven's MS Presentation tomorrow.
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Date: Thursday, March 8, 2007
Time: 3:00 p.m.
Place: Ryerson 251
M.S. Candidate: Bradley Boven
M.S. Paper Title: Dialog Act Tagging in Multi-Party Dialogs
Abstract:
Dialog acts are a key component of language that provide a basis for
spoken language understanding between humans. A dialog act (DA)
represents the meaning of an utterance at the level of an
illocutionary force [Stolcke et al.,2000]. They convey not the
semantic meaning of an utterance, but rather the function an
utterance is intended to serve. Examples of these labels include
questions, statements, backchannels, floor controls, and
disruptions. This paper presents an analysis of word and feature
space models which can be used to construct a dialog act tagger using
both supervised and unsupervised learning techniques. The study was
performed using data from the International Computer Science
Institute (ICSI) Meeting Recorder Dialog Act (MRDA) Corpus.
A natural representation for this tagging problem is a vector space
model of features. Each vector represents a specific dialog act whose
values are the co-occurrence frequencies of each word, n-gram, or
other feature in the model. The resulting ambient space is very high
dimensional, although most of the matrix is sparse. However, we
believe the true latent structure of the space to actually be of a
much lower dimension. Dimensionality reduction techniques attempt to
reveal the dominant underlying structure of these models, while at
the same time also conveniently compact the feature space. This
results in a more computationally manageable space for the
classification algorithms. We augment the feature representation
with n-gram language models, acoustic features, speaker information,
and also dialog act tag information. This paper contrasts different
reduction techniques, including latent semantic analysis and locality
preserving projections. The classifiers utilized employ both
supervised and unsupervised approaches. Specifically, we use
unsupervised clustering techniques, such as k-means and spectral
methods and also a supervised approach using a k-nearest neighbor
classifier.
This paper attempts to understand what features play an important
role in the tagging of dialog acts, what reductions are most
effective at mapping the feature space to improve DA classification
accuracy, and what classification algorithms accurately separate the
data given these inputs. Our best results were achieved using high
order n-gram features in combination with algorithms using local
neighborhood spaces.
Advisors: Professors Gina-Anne Levow and John Goldsmith
A draft copy of Brad Boven's MS Paper is available in Ry 161A.
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Margaret P. Jaffey margaret at cs.uchicago.edu
Department of Computer Science
Student Support Rep (Ry 161A) (773) 702-6011
The University of Chicago http://www.cs.uchicago.edu
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