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An Algorithm for Fuzzy-based Sentence-level Document Clustering for Micro-level Contradiction Analysis by R. Vasanth Kumar Mehta; B. Sankarasubramaniam; S. Rajalakshmi is a scholarly article available to read on EtoBox.

What is An Algorithm for Fuzzy-based Sentence-level Document Clustering for Micro-level Contradiction Analysis about?

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 microlevel contradictions are vital. In this paper, we propose an algorithm for identifying contradictory documents using sentencelevel clustering technique along with an optimization feature. A novel visualization scheme is also suggested to present the results to an end-user.

Author

R. Vasanth Kumar Mehta; B. Sankarasubramaniam; S. Rajalakshmi

Publisher

ACM

Published

2012

Language

EN