Upali Sathyajith Kohomban

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Word Sense Disambiguation suffers from a long-standing problem of knowledge acquisition bottleneck. Although state of the art supervised systems report good accuracies for selected words, they have not been shown to be promising in terms of scalability. In this paper, we present an approach for learning coarser and more general set of concepts from a sense(More)
Learning word sense classes has been shown to be useful in fine-grained word sense disambiguation [Kohomban and Lee, 2005]. However, the common choice for sense classes, WordNet lexicographer files, are not designed for machine learning based word sense disambiguation. In this work, we explore the use of clustering techniques in an effort to construct sense(More)
— Daily, massive number of pieces of textual information is gathered into Social Media. They comprise a challenging style as they are formed with both slang and formal words. This has become an obstacle for processing texts in Social Media. In this paper we address this issue by introducing a pre-processing pipeline for social media text. For the solution(More)
Accurate decision making is the key to make a business profitable. Decision support systems are used to make the decision making process accurate and easy. Even though there are many business specific decision support systems they cannot be used for general purpose decision making or outside their domain. DecisionAI provides a framework which can be used in(More)
Protein complexes play a vital role in living organisms as they regulate and execute biological processes. As experimental methods of extracting protein complexes are fraught with difficulties, scientists look towards protein complex prediction. However, protein-protein interaction (PPI) data which are used to predict protein complexes are often noisy and(More)
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