Supporting the education evidence portal via text mining

被引：10

作者：

Ananiadou, Sophia ^{[1
,2
]}

Thompson, Paul ^{[1
,2
]}

Thomas, James ^{[3
]}

Mu, Tingting ^{[1
,2
]}

Oliver, Sandy ^{[3
]}

Rickinson, Mark ^{[3
]}

Sasaki, Yutaka ^{[1
,2
]}

Weissenbacher, Davy ^{[1
,2
]}

McNaught, John ^{[1
,2
]}

机构：

[1] Univ Manchester, Sch Comp Sci, Manchester M1 7DN, Lancs, England

[2] Univ Manchester, Natl Ctr Text Min, Manchester M1 7DN, Lancs, England

[3] Univ London, EPPI Ctr, Social Sci Res Unit, Inst Educ, London WC1H 0AL, England

来源：

PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES | 2010年 / 368卷 / 1925期

关键词：

text mining; term extraction; document classification; document clustering;

D O I：

10.1098/rsta.2010.0152

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

The UK Education Evidence Portal (eep) provides a single, searchable, point of access to the contents of the websites of 33 organizations relating to education, with the aim of revolutionizing work practices for the education community. Use of the portal alleviates the need to spend time searching multiple resources to find relevant information. However, the combined content of the websites of interest is still very large (over 500 000 documents and growing). This means that searches using the portal can produce very large numbers of hits. As users often have limited time, they would benefit from enhanced methods of performing searches and viewing results, allowing them to drill down to information of interest more efficiently, without having to sift through potentially long lists of irrelevant documents. The Joint Information Systems Committee (JISC)-funded ASSIST project has produced a prototype web interface to demonstrate the applicability of integrating a number of text-mining tools and methods into the eep, to facilitate an enhanced searching, browsing and document-viewing experience. New features include automatic classification of documents according to a taxonomy, automatic clustering of search results according to similar document content, and automatic identification and highlighting of key terms within documents.

引用

页码：3829 / 3844

页数：16