Abstract
One of the main sources of information in our society is a written word. Since times of Sumerians, a written document became the main tool to inform, to teach, to entertain and to archive the knowledge. Today, some 6000 years after Sumerians, nothing has changed with respect to the importance of a written text. To become widely available, the knowledge must be manipulated in an easy and reliable way, and some type of text encoding on a computer is needed
The Latent Semantic Indexing (LSI) is a concept-based automatic indexing method for overcoming the two fundamental problems which exist in the traditional lexicalmatching retrieval schemes: synonymy and polysemy. It is based on the modeling of a term – document relationship using the reduced-dimension representation of a term-document matrix computed by its partial Singular Value Decomposition (SVD).We describe main principles of the LSI in the form of a mathematical model and discuss its implementation on a parallel computer with distributed memory.
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Okša, G., Vajteršic, M. (2009). Parallel SVD Computing in the Latent Semantic Indexing Applications for Data Retrieval. In: Trobec, R., Vajteršic, M., Zinterhof, P. (eds) Parallel Computing. Springer, London. https://doi.org/10.1007/978-1-84882-409-6_12
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DOI: https://doi.org/10.1007/978-1-84882-409-6_12
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