February 8th
3:00pm
PAI 3.14
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Improving Machine Learning Approaches to Noun Phrase Coreference Resolution
Prof. Claire Cardie
Cornell University
This talk will first introduce noun phrase coreference resolution,
one of the critical problems that currently limit performance for many
practical natural language processing tasks. We then present a machine
learning-based solution to noun phrase coreference that extends earlier
work in the area and produces the best empirical results to date ---
for both learning- and knowledge-based approaches to the problem ---
on two standard coreference data sets. Performance gains accrue from
two very different sources of change: first, we propose and evaluate
three extra-linguistic modifications to the machine learning framework;
second, we more than triple the number of linguistic knowledge sources
made available to the learning algorithm. We conclude with a discussion
of why we view these seemingly promising results as ultimately disappointing,
and identify a key area of research where progress in noun phrase coreference
resolution is likely to be made.
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February 15th
3:00pm
ACES 2.402
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Semantics, Pragmatics and Rhetorical Structure
Prof. Nicholas Asher
UT Department of Philosophy
This talk will introduce issues in discourse interpretation. I'll trace
a short history of semantics from Montague Grammar to Dynamic Semantics
and beyond. I'll concentrate on the architecture of a theory of discourse
interpretation that exploits rhetorical function, and I'll give some
applications, time permitting, to dialogue and quantification.
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March 1st
3:00pm
ACES 2.402
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Using Ideal Observer Analysis to Understanding Human Spatial Navigation
Prof. Brian J. Stankiewicz
University of Texas at Austin Department of Psychology
Center for Perceptual Systems
Humans possess a remarkable ability to navigate through complex environments
with relative ease and accuracy. As such, they provide us with both
an existence proof that a robust spatial navigation system can be built
along with a working system that can be reversed engineered. In this
talk I will describe a series of studies investigating human spatial
navigation performance in complex indoor environments. The studies will
investigate the effect of increasing layout size on spatial navigation
performance along with the effect of reducing visual information on
navigation performance. Because we are manipulating the information
available to the subject in these experiments it is important to determine
what changes in performance are due to limitations in human processing
(e. g., memory, strategy, etc.) and what changes are simply due to task
demands (e.g., increased uncertainty). To make this differentiation,
I will also describe an ideal navigator model of indoor spatial navigation
that uses principles from Partially Observable Markov Decision Process
to provide an upper limit on navigation performance in each of these
studies. Human performance will be compared against the ideal observer's
performance to provide an navigation efficiency measure.
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April 12th
3:00pm
ACES 2.402
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Semi-supervised Clustering
Sugato Basu and Mikhail Y. Bilenko
Department of Computer Sciences
University of Texas at Austin
Clustering is a type of unsupervised learning that involves partitioning
a set of unlabeled objects into meaningful groups. In semi-supervised
clustering, some labeled data is used along with the unlabeled data
to obtain a better clustering. We propose to incorporate initial supervision
into clustering in two ways: (1) Learning meaningful distance metrics
from labeled data, and (2) Initializing and constraining the clustering
algorithm with labeled data. We present some initial results on each
of the two methods and propose a unified framework which can be applied
to several domains, including text and biological data.
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