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Experience Mining: Building a Large-Scale Database of Personal Experiences and Opinions from Web Documents., , , , , , , , and . Web Intelligence, page 314-321. IEEE Computer Society, (2008)Investigating the Effectiveness of Laplacian-Based Kernels in Hub Reduction., , , , and . AAAI, AAAI Press, (2012)Unsupervised WSD by Finding the Predominant Sense Using Context as a Dynamic Thesaurus., , , and . J. Comput. Sci. Technol., 25 (5): 1030-1039 (2010)Walk-based Computation of Contextual Word Similarity., , , and . COLING, page 1081-1096. Indian Institute of Technology Bombay, (2012)Mining personal experiences and opinions from Web documents., , , , , , , , and . Web Intell. Agent Syst., 9 (2): 109-121 (2011)Bayesian Optimization With an Auxiliary Classifier for the Development of Polymer Materials., , , , , and . IEEE BigData, page 6014-6016. IEEE, (2021)Annotating Cohesive Statements of Anatomical Knowledge Toward Semi-automated Information Extraction., , , and . KDIR, page 342-347. SciTePress, (2014)Localized Centering: Reducing Hubness in Large-Sample Data., , , , , and . AAAI, page 2645-2651. AAAI Press, (2015)Flattening the Density Gradient for Eliminating Spatial Centrality to Reduce Hubness., , , , and . AAAI, page 1659-1665. AAAI Press, (2016)Reducing Hubness: A Cause of Vulnerability in Recommender Systems., , , and . SIGIR, page 815-818. ACM, (2015)