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	title        = {Combining Relational and Distributional Knowledge for Word Sense Disambiguation},
	abstract     = {We present a new approach to word sense
disambiguation derived from recent ideas
in distributional semantics. The input to
the algorithm is a large unlabeled corpus and a graph describing how senses
are related; no sense-annotated corpus is
needed. The fundamental idea is to embed meaning representations of senses in
the same continuous-valued vector space
as the representations of
words. In this way, the knowledge encoded in the lexical resource is combined with the infor-
mation derived by the distributional methods. Once this step has been carried out,
the sense representations can be plugged
back into e.g. the skip-gram model, which
allows us to compute scores for the different possible senses of a word in a given

We evaluated the new word sense disambiguation system on two Swedish test
sets annotated with senses defined by the
SALDO lexical resource. In both evaluations, our system soundly outperformed
random and first-sense baselines. Its accuracy was slightly above that of a well-
known graph-based system, while being
computationally much more efficient,},
	booktitle    = {Proceedings of the 20th Nordic Conference of Computational Linguistics, May 12-13, Vilnius, Lithuania. Linköping Electronic Conference Proceedings 109, Linköping University Electronic Press..},
	author       = {Johansson, Richard and Nieto Piña, Luis},
	year         = {2015},
	ISBN         = {978-91-7519-098-3},
	pages        = {69--78},