  Word Sense Disambiguation is an open problem in Natural Language Processing
which is particularly challenging and useful in the unsupervised setting where
all the words in any given text need to be disambiguated without using any
labeled data. Typically WSD systems use the sentence or a small window of words
around the target word as the context for disambiguation because their
computational complexity scales exponentially with the size of the context. In
this paper, we leverage the formalism of topic model to design a WSD system
that scales linearly with the number of words in the context. As a result, our
system is able to utilize the whole document as the context for a word to be
disambiguated. The proposed method is a variant of Latent Dirichlet Allocation
in which the topic proportions for a document are replaced by synset
proportions. We further utilize the information in the WordNet by assigning a
non-uniform prior to synset distribution over words and a logistic-normal prior
for document distribution over synsets. We evaluate the proposed method on
Senseval-2, Senseval-3, SemEval-2007, SemEval-2013 and SemEval-2015 English
All-Word WSD datasets and show that it outperforms the state-of-the-art
unsupervised knowledge-based WSD system by a significant margin.
