University of Cambridge > Talks.cam > NLIP Seminar Series > Learning to Classify Noun-Noun Semantic Relations

Learning to Classify Noun-Noun Semantic Relations

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  • UserDiarmuid O'Seaghdha, Computer Laboratory, University of Cambridge
  • ClockFriday 01 February 2008, 12:00-13:00
  • HouseSW01 Computer Laboratory.

If you have a question about this talk, please contact Johanna Geiss.

Identifying the semantic relations between entities mentioned in a sentence is an important NLP task. Variants of the task crop up in many guises. One of these is the problem of classifying the semantic relation in a noun-noun compound (e.g. “kitchen table” is a locational compound, whereas “plastic table” describes the composition of the table). This problem has received a lot of attention in recent years but remains difficult to solve, in part because standard relation classifcation methods fall down when the context of the entity mentions do not make the semantic relation explicit.

I’ll be talking about the kind of corpus-driven/machine-learning methods we can use for classifying semantic relations in compounds and whether these methods extend to more standard relation tasks such as the Semeval 2007 task on “Classification of Semantic Relations between Nominals”. In particular I’ll describe kernels on vectors, strings, trees and sets and how multiple kernels can be combined to integrate different representations of the data at hand.

This talk is part of the NLIP Seminar Series series.

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