Detailed Information on Publication Record

ŘEHŮŘEK, Radim. Speeding Up Latent Semantic Analysis: A Streamed Distributed Algorithm for SVD Updates. Online. In Joaquim Filipe. Proceedings of the 3rd International Conference on Agents and Artificial Intelligence (ICAART). Portugal: INSTICC Press, 2010. p. 446-451. ISBN 978-989-8425-40-9. [citováno 2024-04-23]

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Basic information
Original name	Speeding Up Latent Semantic Analysis: A Streamed Distributed Algorithm for SVD Updates
Authors	ŘEHŮŘEK, Radim (203 Czech Republic, guarantor, belonging to the institution)
Edition	Portugal, Proceedings of the 3rd International Conference on Agents and Artificial Intelligence (ICAART), p. 446-451, 6 pp. 2010.
Publisher	INSTICC Press

Other information
Original language	English
Type of outcome	Proceedings paper
Field of Study	10000 1. Natural Sciences
Country of publisher	Italy
Confidentiality degree	is not subject to a state or trade secret
WWW	URL
RIV identification code	RIV/00216224:14330/10:00047038
Organization unit	Faculty of Informatics
ISBN	978-989-8425-40-9
Keywords in English	svd lda lsi
Tags	International impact, Reviewed
Changed by	Changed by: RNDr. Radim Řehůřek, Ph.D., učo 39672. Changed: 28/2/2011 05:41.

Abstract
The purpose of Latent Semantic Analysis (LSA) is to find hidden (latent) structure in a collection of texts represented in the Vector Space Model. LSA was introduced in~\cite{deerwester1990indexing} and has since become a standard tool in the field of Natural Language Processing and Information Retrieval. At the heart of LSA lies the \emph{Singular Value Decomposition} algorithm, which makes LSA (sometimes also called Latent Semantic Indexing, or LSI) really just another member of the broad family of applications that make use of SVD's robust and mathematically well-founded approximation capabilities, from Image Processing; or Signal Processing, where SVD is commonly used to separate signal from noise. SVD is also used in solving shift-invariant differential equations, in Geophysics, in Antenna Array Processing, \ldots}. In this way, although we will discuss our results in the perspective and terminology of LSA and Natural Language Processing, our results are in fact applicable to a wide range of problems and domains across much of the field of Computer Science.

Abstract

The purpose of Latent Semantic Analysis (LSA) is to find hidden (latent) structure in a collection of texts represented in the Vector Space Model. LSA was introduced in~\cite{deerwester1990indexing} and has since become a standard tool in the field of Natural Language Processing and Information Retrieval. At the heart of LSA lies the \emph{Singular Value Decomposition} algorithm, which makes LSA (sometimes also called Latent Semantic Indexing, or LSI) really just another member of the broad family of applications that make use of SVD's robust and mathematically well-founded approximation capabilities, from Image Processing; or Signal Processing, where SVD is commonly used to separate signal from noise. SVD is also used in solving shift-invariant differential equations, in Geophysics, in Antenna Array Processing, \ldots}. In this way, although we will discuss our results in the perspective and terminology of LSA and Natural Language Processing, our results are in fact applicable to a wide range of problems and domains across much of the field of Computer Science.

Links
LC536, research and development project	Name: Centrum komputační lingvistiky
LC536, research and development project	Investor: Ministry of Education, Youth and Sports of the CR, Centrum komputační lingvistiky