[Lisa_seminaires] Fwd: UdeM-McGill-MITACS machine learning seminar

Dumitru Erhan dumitru.erhan at umontreal.ca
Jeu 26 Mar 18:21:32 EDT 2009


Reminder:


---------- Forwarded message ----------
From: Dumitru Erhan <dumitru.erhan at umontreal.ca>
Date: Mon, Mar 23, 2009 at 15:37
Subject: [Lisa_seminaires] UdeM-McGill-MITACS machine learning seminar
Fri March 27th, 15:00,
To: lisa_seminaires at iro.umontreal.ca, Jeremy Barnes <jeremy at barneso.com>


Friday's seminar (see
http://www.iro.umontreal.ca/article.php3?id_article=107&lang=en):

Machine Learning and Computational Linguistics: Tools of the Trade
(or, The Data Acquisition Mental Bottleneck)

by Jeremy Barnes

Location: Pavillon André-Aisenstadt (UdeM), room 3195
Time: March 27th, 15h00

Computational Linguistics is a field where Machine Learning algorithms
are ubiquitously applied.  However, the gap between a good idea or a
published paper and a commercial system is often large.

The kinds of problems that can beset a machine learning practitioner
in this field will be dissected and the tools and techniques used to
overcome them explored.

A particular emphasis is placed on the mapping of linguistics problems
onto a machine learning problem and the complexities associated with
obtaining and using linguistic datasets.

In addition, common wisdom such as the "Data Acquisition Bottleneck"
is sometimes cited both as a reason for the ineffectiveness of a
machine learning solution and for abandoning promising approaches.
This bottleneck will be analyzed and shown to be maybe more of a
mental than a practical bottleneck.

The seminar should be relevant to both computational linguists and
machine learning practitioners, as well as anyone who is interested in
the large-scale use of machine learning to solve problems in an
industry environment.  The tone will be relatively informal.

Bio:

Jeremy Barnes recently finished a 8 1/2 year stint at Idilia Inc, a
Montreal-based company that has developed and commercialized Word Sense
Disambiguation, knowledge extraction and paraphrasing technology see
(www.idilia.com).  Beginning as the first R&D employee in the company,
he artitected the technology around machine learning and has applied
machine learning to numerous unsolved or semi-solved problems in the
domain.
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