Speeding Up Latent Semantic Analysis: A Streamed Distributed
Algorithm for SVD Updates

D 2010

Speeding Up Latent Semantic Analysis: A Streamed Distributed Algorithm for SVD Updates

ŘEHŮŘEK, Radim

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

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10000 1. Natural Sciences

Country of publisher

Italy

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

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

Abstract

V originále

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

Investor: Ministry of Education, Youth and Sports of the CR, Centrum komputační lingvistiky

Citovat

ŘEHŮŘEK, Radim. Speeding Up Latent Semantic Analysis: A Streamed Distributed Algorithm for SVD Updates. 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.

@inproceedings{921969,
   author = {Řehůřek, Radim},
   address = {Portugal},
   booktitle = {Proceedings of the 3rd International Conference on Agents and Artificial Intelligence (ICAART)},
   editor = {Joaquim Filipe},
   keywords = {svd lda lsi},
   language = {eng},
   location = {Portugal},
   isbn = {978-989-8425-40-9},
   pages = {446-451},
   publisher = {INSTICC Press},
   title = {Speeding Up Latent Semantic Analysis: A Streamed Distributed Algorithm for SVD Updates},
   url = {http://www.icaart.org/Program_Saturday.htm},
   year = {2010}
}

TY  - JOUR
ID  - 921969
AU  - Řehůřek, Radim
PY  - 2010
TI  - Speeding Up Latent Semantic Analysis: A Streamed Distributed Algorithm for SVD Updates
PB  - INSTICC Press
CY  - Portugal
SN  - 9789898425409
KW  - svd lda lsi
UR  - http://www.icaart.org/Program_Saturday.htm
N2  - 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.
ER  -

ŘEHŮŘEK, Radim. Speeding Up Latent Semantic Analysis: A Streamed Distributed Algorithm for SVD Updates. In Joaquim Filipe. \textit{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.

Detailed Information on Publication Record