Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Word Sense Disambiguation: Algorithms and Applications
Word Sense Disambiguation: Algorithms and Applications
Multiwords and word sense disambiguation
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
A broad evaluation of techniques for automatic acquisition of multiword expressions
ACL '12 Proceedings of ACL 2012 Student Research Workshop
A generic framework for multiword expressions treatment: from acquisition to applications
ACL '12 Proceedings of ACL 2012 Student Research Workshop
Modeling the internal variability of multiword expressions through a pattern-based method
ACM Transactions on Speech and Language Processing (TSLP) - Special issue on multiword expressions: From theory to practice and use, part 1
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Multi-Word Expressions (MWEs) are prevalent in text and are also, on average, less polysemous than mono-words. This suggests that accurate MWE detection should lead to a non-trivial improvement in Word Sense Disambiguation (WSD). We show that a straightforward MWE detection strategy, due to Arranz et al. (2005), can increase a WSD algorithm's baseline f-measure by 5 percentage points. Our measurements are consistent with Arranz's, and our study goes further by using a portion of the Semcor corpus containing 12,449 MWEs - over 30 times more than the approximately 400 used by Arranz. We also show that perfect MWE detection over Semcor only nets a total 6 percentage point increase in WSD f-measure; therefore there is little room for improvement over the results presented here. We provide our MWE detection algorithms, along with a general detection framework, in a free, open-source Java library called jMWE.