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.