Speaker: Behrang Mohit
Semantic Extraction is an NLP task that pertains to the assignment of
semantic bindings to short units of text (usually sentences). NLP problems
such as Information Extraction, Question Answering Systems and Text
Classification Systems could benefit from Semantic Extraction. We have
used two manually-built knowledge bases (WordNet and FrameNet) to automate
Semantic Extraction.
In my presentation, I will give an overview of the FrameNet project and
then talk about my work with Srini Narayanan on Semantic Extraction. I
presented this work last summer as a short paper in NAACL-HLT 2003. The
paper can be downloaded from:
http://www.cs.pitt.edu/~behrang/MohitNarayananHLT2003.pdf
Speakers: Diane Litman and Kate Forbes
Abstract:
We investigate the automatic classification of student emotional
states in a corpus of human-human spoken tutoring dialogues. We
first annotated student turns in this corpus for negative, neutral and
positi ve emotions. We then automatically extracted acoustic and
prosodic features from the student speech, and compared the results of
a variety of machine learning algorithms that use 8 different feature
sets to predict the annotated emotions. Our best results have an
accuracy of 80.53% and show 26.28% relative improvement over a
baseline. These results suggest that th e intelligent tutoring spoken
dialogue system we are developing can be enhanced to automatically
predict and adapt to student emotional states.
This will be an early practice talk for a paper that
will be presented in December at ASRU.
Speaker: Wendy Chapman
Abstract
Biosurveillance systems use electronic patient medical information to
monitor for possible natural or bioterristic outbreaks. Currently, the only
information used by these systems is a patient's triage chief complaint,
which is a short phrase describing the patient's reason for coming to an
emergency room. To monitor for specific diseases or syndromes like Severe
Acute Respiratory Syndrome (SARS) or pneumonia, more specific clinical
information needs to be gathered. That information is in free-text patient
reports.
I will describe a project I embarked on this summer at the National Library
of Medicine in which I applied an NLP indexing tool called MetaMap that was
created for the literature to the task of identifying respiratory findings
from emergency department reports.