NDLI: Turkish Document Classification Using Shorter Roots

Content Provider	IEEE Xplore Digital Library
Author	Cataltepe, Z. Turan, Y. Kesgin, F.
Copyright Year	2007
Description	Author affiliation: Istanbul Teknik Univ., Istanbul, Turkey (Cataltepe, Z.; Turan, Y.; Kesgin, F.)
Abstract	Stemming is one of commonly used pre-processing steps in document categorization. Especially when fast and accurate classification of a lot of documents is needed, it is important to have as small number of and as small length roots as possible. This would not only reduce the time it takes to train and test classifiers but also would reduce the storage requirements for each document. In this study, we analyze the performance of classifiers when the longest or shortest roots found by a stemmer are used. We also analyze the effect of using only the consonants in the roots. We use two document data sets, obtained from Milliyet newspaper and Wikipedia to analyze classification accuracy of classifiers when roots obtained under these four conditions are used. We also analyze the classification accuracy when only the first 4, 3 or 2 letters or consonants are used from the roots. Using smaller roots results in smaller number of TF-IDF vectors. Especially for small sized TF-IDF vectors, using only consonants in the roots gives better performance than using all letters in the roots.
Starting Page	1
Ending Page	4
File Size	907023
Page Count	4
File Format	PDF
ISBN	1424407192
DOI	10.1109/SIU.2007.4298786
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-06-11
Publisher Place	Turkey
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Frequency Wikipedia Performance analysis Testing
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Automatic summarization of Turkish documents using non-negative matrix factorization

Short Text Classification Using Wikipedia Concept Based Document Representation

Exploiting Negative Categories and Wikipedia Structures for Document Classification

Exploiting Turkish Wikipedia as a semantic resource for text classification

Using wikipedia with associative networks for document classification.

Identifying Document Topics Using the Wikipedia Category Network

Advanced Internet Protocols and

Term frequency dynamics in collaborative articles

Document Indexing and Retrieval Using Wikipedia

Turkish Document Classification Using Shorter Roots

Similar Documents

Automatic summarization of Turkish documents using non-negative matrix factorization

Short Text Classification Using Wikipedia Concept Based Document Representation

Exploiting Negative Categories and Wikipedia Structures for Document Classification

Exploiting Turkish Wikipedia as a semantic resource for text classification

Using wikipedia with associative networks for document classification.

Identifying Document Topics Using the Wikipedia Category Network

Advanced Internet Protocols and

Term frequency dynamics in collaborative articles

Document Indexing and Retrieval Using Wikipedia

Turkish Document Classification Using Shorter Roots