Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
The Evaluation of Sentence Similarity Measures
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Document clustering using nonnegative matrix factorization
Information Processing and Management: an International Journal
Summarizing contrastive viewpoints in opinionated text
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
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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.