Using Random Indexing to improve Singular Value Decomposition for Latent Semantic Analysis

被引：0

作者：

Sellberg, Linus ^{[1
]}

Jonsson, Arne ^{[1
]}

机构：

[1] Linkoping Univ, Dept Comp & Informat Sci, Santa Anna IT Res Inst AB, SE-58183 Linkoping, Sweden

来源：

SIXTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, LREC 2008 | 2008年

关键词：

D O I：

暂无

中图分类号：

H0 [语言学];

学科分类号：

030303 ; 0501 ; 050102 ;

摘要：

We present results from using Random Indexing for Latent Semantic Analysis to handle Singular Value Decomposition tractability issues. We compare Latent Semantic Analysis, Random Indexing and Latent Semantic Analysis on Random Indexing reduced matrices. In this study we use a corpus comprising 1003 documents from the MEDLINE-corpus. Our results show that Latent Semantic Analysis on Random Indexing reduced matrices provide better results on Precision and Recall than Random Indexing only. Furthermore, computation time for Singular Value Decomposition on a Random Indexing reduced matrix is almost halved compared to Latent Semantic Analysis.

引用

页码：2335 / 2338

页数：4

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