To analyze and explore large textual corpus, we are generally limited by the available main memory. This may lead to a proliferation of processor load due to greedy computing. The authors propose to deal with this problem to compute co-similarities from large-scale documents. The authors propose to enhance co-similarity learning by upstream and downstream parallel computing. The first deploys the fuzzy linear model in a Grid environment. The second deals with multi-view datasets while introducing different architectures by using several instances of a fuzzy triadic similarity algorithm.

The contribution of this work relates to the field of Arabic text-based document analysis for the detection of plagiarism. This analysis will be carried out according to the triadic computation model of document similarity. The authors propose a hybrid segmentation prototype for Arabic text-based documents that links different processing steps in order to generate the similarity rate between the documents of an Arabic corpus. It involves two segmentation systems and a morphological analysis in order to obtain a matrix representation adapted to the triadic similarity computation according to three abstraction levels: documents, sentences and words.

This work exploits the use of Triadic Concept Analysis (TCA) for document retrieval to efficiently answer users' queries. The proposed conceptual analysis aims at describing the documents according to three hierarchical levels with a triadic computing model. It is based on normalisation and prototyping, which, by projection, induce a formal dyadic context. This representation has enabled us to visualize triadic concepts associated with the documents, sentences and words through the construction of fuzzy concept lattice. The lattices generated are then used to organize documents in a hierarchical structure to facilitate retrieval process.