An algorithm for fuzzy-based Sentence-level Document Clustering for Micro-level Contradiction Analysis

Contradiction Analysis is one of the popular text-mining operations in which a document whose content is contradictory to the theme of a set of documents is identified. It is a means to identifying Outlier documents that do not confirm to the overall sense conveyed by other documents. Most of the existing techniques perform document-level comparisons, ignoring the sentence-level semantics, often leading to loss of vital information. Applications in domains like Defence and Healthcare require high levels of accuracy and identification of micro-level contradictions are vital. In this paper, we propose an algorithm for identifying contradictory documents using sentence-level clustering technique along with an optimization feature. A novel visualization scheme is also suggested to present the results to an end-user.