Seminario MAVIR: Satoshi Sekine |
Seminario MAVIRSatoshi Sekine: Minimally Supervised Knowledge Discoverymiércoles 14 de noviembre de 2007, 16h00-17h30.
Lugar de celebraciónEscuela Técnica Superior de Ingenieros de TelecomunicaciónSalón de Grados, Edificio A. Avenida Complutense s/n Ciudad Universitaria E28040 Madrid Cómo llegar y planos. La asistencia es libre y gratuita. Programa completo . Resumen
The crucial problem in creating systems in semantic domain is that a vast amount of knowledge is needed to create reasonable system. In other words, we have to overcome the sparseness problem and scalability problem. In order to solve it, we believe minimally supervised (i.e. unsupervised and/or semisupervised) learning methods utilizing a large un annotate corpus would be a possible solution. The corpus available in electric form at this moment is vast enough to make us believe that the knowledge to understand most of the world is written down consciously or unconsciously somewhere in the corpus. The new paradigm is aiming at a reformulation of the knowledge scattered in the corpus into the shape in which a system can use to solve a task. In the talk, I would like to summarize the studies which have been conducted in the field. It would hopefully give the audience the overview of the field and possible research directions in the future. I will also descrive several own studies.
Satoshi SekineSatoshi Sekine is an Assistant Research Professor at New York University. He received his MSc at UMIST, UK in 1992 and his PhD in 1998 at NYU. He has been working on various topics, including parser, NE, Information Extraction and minimally supervised knowledge discovery. Recently, he organized Workshop on Textual Entailment and Paraphrasing (ACL07), Web People Search task at SemEval07 and served as guest editor on the special issue on Named Entity. He lead a middle size NSF project for 5 years on OnDemand Information Extraction.
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