Spectral Learning with Type-2 Fuzzy Numbers for Question/Answering System

Graph-based semi-supervised learning has recently emerged as a promising approach to data-sparse learning problems in natural language processing. They rely on graphs that jointly represent each data point. The problem of how to best formulate the graph representation remains an open research topic. In this paper, we introduce a type-2 fuzzy arithmetic to characterize the edge weights of a formed graph as type-2 fuzzy numbers. The fuzzy numbers are identified by the changing parameters of the fuzzy kernel nearest neighbor algorithm, namely the degree of fuzziness and the hyper-parameter of the Gaussian kernel function, both of which have an effect on the uncertainty in forming the affinity matrix of the graph. We introduce a new graph-based semi-supervised learning with the type-2 arithmetic operations. We apply this technique in the framework of label propagation and evaluate on a question answering task. We demonstrate that the type-2 SSL can improve the prediction accuracy and can be considered to be the an alternative tool for text mining applications of computational linguistics. Keywords—Graph-based semi-supervised learning, kernel fuzzy k-nearest neighbor, type-2 fuzzy numbers.