  One of the key issues in both natural language understanding and generation
is the appropriate processing of Multiword Expressions (MWEs). MWEs pose a huge
problem to the precise language processing due to their idiosyncratic nature
and diversity in lexical, syntactical and semantic properties. The semantics of
a MWE cannot be expressed after combining the semantics of its constituents.
Therefore, the formalism of semantic clustering is often viewed as an
instrument for extracting MWEs especially for resource constraint languages
like Bengali. The present semantic clustering approach contributes to locate
clusters of the synonymous noun tokens present in the document. These clusters
in turn help measure the similarity between the constituent words of a
potentially candidate phrase using a vector space model and judge the
suitability of this phrase to be a MWE. In this experiment, we apply the
semantic clustering approach for noun-noun bigram MWEs, though it can be
extended to any types of MWEs. In parallel, the well known statistical models,
namely Point-wise Mutual Information (PMI), Log Likelihood Ratio (LLR),
Significance function are also employed to extract MWEs from the Bengali
corpus. The comparative evaluation shows that the semantic clustering approach
outperforms all other competing statistical models. As a by-product of this
experiment, we have started developing a standard lexicon in Bengali that
serves as a productive Bengali linguistic thesaurus.
