[Lisa_seminaires] Next week - 2 UdeM-McGill-MITACS seminars!

Pascal Lamblin lamblinp at iro.umontreal.ca
Mer 12 Nov 16:45:55 EST 2008


Hi everyone,

Next week, we're lucky to have two invited speakers giving a talk at
UdeM, the first on Monday, the second on Friday, both at 15:00.

Descriptions of both talks follow.

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Title:    Cheap and Fast  But is it Good?
          Evaluating Nonexpert Annotations for Machine Learning Tasks
Speaker:  Rion Snow
Date:     Monday, November 17th, 15:00
Place:    Room 3195 (pav. André-Aisenstadt, UdeM)

Abstract:
Human annotation is crucial for many machine learning tasks but can be
expensive and time-consuming. We explore the use of Amazon's Mechanical
Turk web service, a significantly cheaper and faster method for
collecting annotations from a broad base of paid non-expert contributors
over the Web. We investigate five task in the field of natural language
processing: affect recognition, word similarity, recognizing textual
entailment, event temporal ordering, and word sense disambiguation. For
all five, we show high agreement between Mechanical Turk non-expert
annotations and existing gold standard labels provided by expert
labelers. For the task of affect recognition, we also show that using
non-expert labels for training machine learning algorithms can be as
effective as using gold standard annotations from experts. We propose a
technique for bias correction that significantly improves annotation
quality on two tasks. We conclude that many large labeling tasks can be
effectively designed and carried out in this method at a fraction of the
usual expense.

A summary of this work may be found online at:
http://blog.doloreslabs.com/2008/09/amt-fast-cheap-good-machine-learning/.

Bio:
Rion Snow is a PhD Candidate in Computer Science at Stanford University,
advised by Professors Andrew Ng and Dan Jurafsky. Rion works in the
intersection of machine learning and natural language processing, with a
focus in computational semantics. He leads the Stanford Wordnet Project,
which aims at learning large-scale semantic networks automatically from
natural text. His work on automatically inferring semantic taxonomies
received the Best Paper Award at the 2006 conference for the Association
of Computational Linguistics. His publications and contact information
may be found at his home page: http://ai.stanford.edu/~rion/.

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Title:    Analysis of EEG data by means of ordinal pattern distributions
Speaker:  Mathieu Sinn
Date:     Friday, November 21st, 15:00
Place:    Room 3195 (pav. André-Aisenstadt, UdeM)

Abstract:
Ordinal time series analysis is a new fast and robust approach to the
investigation of long and complex time series, such as
electroencephalographic (EEG) data. The basic idea is to consider
locally the order relations between the values instead of the values
themselves.

An ordinal pattern represents the order relations between a finite
number of equidistant values of a time series. Assuming that these
values are pairwise different, their unique rank order can be simply
described by a permutation. By looking at the distributions of ordinal
patterns (or measures derived from the distributions) in different parts
of a time series, changes in the dynamic of the underlying system can be
detected, such as increasing or decreasing complexity. One derived
measure is the permutation entropy, which is the Shannon entropy of
ordinal pattern distributions. It has been shown that in certain cases
of time series obtained by dynamical systems, the permutation entropy is
related to the Kolmogorov-Sinai entropy which is a well-known measure
for the complexity of time series and the underlying systems,
respectively.

A main application of the permutation entropy so far has been the
detection of epileptic activity in EEG data, as it is widely accepted
that the complexity of the brain dynamics changes (namely, decreases)
during epileptic seizures. However, since clearly different ordinal
pattern distributions may yield very similar permutation entropy values,
we propose to take the whole distributions into account. First attempts
show that a cluster analysis of ordinal pattern distributions may be a
good approach to an automatic separation and classification of EEG data.
Beneath the detection and localization of epileptic activity, another
interesting application of this method is the automatic classification
of sleep stages according to the criterions of Rechtschaffen and Kales.

This is joint work with Karsten Keller.

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Hoping to see you there,
-- 
Pascal


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