Evaluating keyword selection methods for WEBSOM text archives

Author

Azcarraga, Arnulfo P. ; Yap, Teddy N., Jr. ; Tan, Jonathan ; Chua, Tat Seng

Author_Institution

Sch. of Inf. Technol. & Comput., De La Salle Univ.-Canlubang, Laguna, Philippines

Volume

16

Issue

3

fYear

2004

fDate

3/1/2004 12:00:00 AM

Firstpage

380

Lastpage

383

Abstract

The WEBSOM methodology, proven effective for building very large text archives, includes a method that extracts labels for each document cluster assigned to nodes in the map. However, the WEBSOM method needs to retrieve all the words of all the documents associated to each node. Since maps may have more than 100,000 nodes and since the archive may contain up to seven million documents, the WEBSOM methodology needs a faster alternative method for keyword selection. Presented here is such an alternative method that is able to quickly deduce meaningful labels per node in the map. It does this just by analyzing the relative weight distribution of the SOM weight vectors and by taking advantage of some characteristics of the random projection method used in dimensionality reduction. The effectiveness of this technique is demonstrated on news document collections.

Keywords

information retrieval systems; self-organising feature maps; text analysis; word processing; SOM weight vectors; WEBSOM text archive; document cluster; keyword selection methods; news document collections; random projection method characteristics; relative weight distribution; Classification algorithms; Clustering algorithms; Computer Society; Frequency; Navigation; Text categorization;

fLanguage

English

Journal_Title

Knowledge and Data Engineering, IEEE Transactions on

Publisher

ieee

ISSN

1041-4347

Type

jour

DOI

10.1109/TKDE.2003.1262193

Filename

1262193