LSI-based semantic characterisation for automated text categorisation

As knowledge acquisition remains a bottleneck, incorporating human judgement within intelligent systems is still a challenge. Supervised learning methods have shown to be able to assist humans in automated text categorization (ATC). However, the performance of such systems is largely dependent on th...

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Main Author:	Tan, Ping Ping
Format:	Thesis
Language:	English
Published:	2009
Subjects:	QA76 Computer software
Online Access:	http://ir.unimas.my/id/eprint/167/8/LSI-based%20semantic%20characterization%20for%20automated%20text%20categorization%20%28fulltext%29.pdf
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id	my-unimas-ir.167
record_format	uketd_dc
spelling	my-unimas-ir.1672023-05-08T07:37:46Z LSI-based semantic characterisation for automated text categorisation 2009 Tan, Ping Ping QA76 Computer software As knowledge acquisition remains a bottleneck, incorporating human judgement within intelligent systems is still a challenge. Supervised learning methods have shown to be able to assist humans in automated text categorization (ATC). However, the performance of such systems is largely dependent on the characteristics of the datasets. Without the understanding of why a classifier works well for certain datasets, it is difficult to generalise its application across domains. Furthermore, most training sets used in supervised ATC have category labels provided by human experts. Expert knowledge used in the task of categorization is often not captured via the mere process of manipulating category labels. This has resulted in lose of intended meanings while performing supervised ATC. Besides that, large text datasets often contain a greater deal of noise. Faculty of Computer Science and Information Technology 2009 Thesis http://ir.unimas.my/id/eprint/167/ http://ir.unimas.my/id/eprint/167/8/LSI-based%20semantic%20characterization%20for%20automated%20text%20categorization%20%28fulltext%29.pdf text en validuser masters Universiti Malaysia Sarawak Faculty of Computer Science and Information Technology
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Tan, Ping Ping LSI-based semantic characterisation for automated text categorisation
description	As knowledge acquisition remains a bottleneck, incorporating human judgement within intelligent systems is still a challenge. Supervised learning methods have shown to be able to assist humans in automated text categorization (ATC). However, the performance of such systems is largely dependent on the characteristics of the datasets. Without the understanding of why a classifier works well for certain datasets, it is difficult to generalise its application across domains. Furthermore, most training sets used in supervised ATC have category labels provided by human experts. Expert knowledge used in the task of categorization is often not captured via the mere process of manipulating category labels. This has resulted in lose of intended meanings while performing supervised ATC. Besides that, large text datasets often contain a greater deal of noise.
format	Thesis
qualification_level	Master's degree
author	Tan, Ping Ping
author_facet	Tan, Ping Ping
author_sort	Tan, Ping Ping
title	LSI-based semantic characterisation for automated text categorisation
title_short	LSI-based semantic characterisation for automated text categorisation
title_full	LSI-based semantic characterisation for automated text categorisation
title_fullStr	LSI-based semantic characterisation for automated text categorisation
title_full_unstemmed	LSI-based semantic characterisation for automated text categorisation
title_sort	lsi-based semantic characterisation for automated text categorisation
granting_institution	Universiti Malaysia Sarawak
granting_department	Faculty of Computer Science and Information Technology
publishDate	2009
url	http://ir.unimas.my/id/eprint/167/8/LSI-based%20semantic%20characterization%20for%20automated%20text%20categorization%20%28fulltext%29.pdf
_version_	1783727871608487936

LSI-based semantic characterisation for automated text categorisation

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