Pre-meeting (Dong Nguyen).
Related paper: Modeling Consensus: Classifier Combination for Word Sense Disambiguation, Radu Florian and David Yarowksy
Focus paper: Tense Sense Disambiguation: a New Syntactic Polysemy Task, Roi Reichart and Ari Rappoport
The related paper by Florian and Yarowsky describe experiments with different methods of combining classifiers to improve the performance on the word sense disambiguation task. They experimented with 6 different classifiers, and different methods of combining them (weighted average of posterior probability, combining based on order statistics and voting). Their features included bigrams, trigrams, BOW, and syntactic features. They showed that high performance gains could be gained by combining them. Their final approach ('stacking'), was a combination of different combinations of classifiers.
I think the main contribution of the paper was combining classifiers on WSD disambiguation. It doesn't seem their features itself are very innovative or was their focus. Furthermore, it's not clear how innovative the used classifier combinations were in general (not restricted to WSD).
The focus paper was interesting, but because I'm not familiar with some of the approaches their work builds on (sequential model, SNOW), some parts were not totally clear to me. Also, I found some of their defined senses seem to be a bit strange (i.e. 'when describing the content of a book' seems to be very specific).