Implementing Random Indexing on GPU

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F11%3APU96124" target="_blank" >RIV/00216305:26230/11:PU96124 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Implementing Random Indexing on GPU
Original language description
Vector space models (also word space models or term space models) are algebraic models, used for representing text documents as vectors of terms. They have received much attention recently as they have wide spectrum of applications, including informationfiltering, information retrieval, indexing and relevancy ranking. They can be advantageous over the other representations because vector spaces are mathematically well defined and there’s large set of tools for manipulating them. Random Indexing is one of methods used for calculating vector space models from set of documents, based on distributional statistics of term cooccurrences. To produce useful results it may therefore require large amounts of data and significant computational power. We present an efficient implementation of Random Indexing on GPU, allowing fast training even on large datasets. It is only limited by amount of memory available on GPU, some techniques to overcome this limitation are sugg
Czech name
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Czech description
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Project
<a href="/en/project/7H10012" target="_blank" >7H10012: Embedded Service Oriented Monitoring, Diagnostics and Control: Towards the Asset-aware and Self-Recovery Factory</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2011
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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