Using Semantic Clustering for Detecting Bengali Multiword Expressions (Informatica, 2014)
Multiword Expressions (MWEs), a known nuisance for both linguistics and NLP, blur the lines betweensyntax and semantics. The semantic of a MWE cannot be expressed after combining the semantic of itsconstituents. In this study, we propose a novel approach called "semantic clustering" as an instrument forextracting the MWEs especially for resource constraint languages like Bengali. At the beginning, it tries tolocate clusters of the synonymous noun tokens present in the document. These clusters in turn help measurethe similarity between the constituent words of a potential candidate using a vector space model. Finallythe judgment for the suitability of this phrase to be a MWE is carried out based on a predefined threshold.In this experiment, we apply the semantic clustering approach only for noun-noun bigram MWEs; howeverwe believe that it can be extended to any types of MWEs. We compare our approach with the state-ofthe-art statistical approach. The evaluation results show that the semantic clustering outperforms all othercompeting methods. As a byproduct of this experiment, we have started developing a standard lexicon inBengali that serves as a productive Bengali linguistic thesaurus.