NDLI: Support Vector Machines for Text Categorization in Chinese Question Classification

Content Provider	IEEE Xplore Digital Library
Author	Xu-Dong Lin Hong Peng Bo Liu
Copyright Year	2006
Description	Author affiliation: Coll. of Comput. Sci. & Eng., South China Univ. of Technol., Guangzhou (Xu-Dong Lin; Hong Peng; Bo Liu)
Abstract	Question classification plays a crucial important role in the question answering system because categorizing a given question is beneficial to identify an answer in the documents. The goal of question classification is to accurately assign labels to question based on expected answer type. Recently, many machine learning algorithms are used for question classification. However many research results show that SVM perform best in this task, because it is well known to work well for nonlinear, sparse, high dimensional problems. In this experiment, we perform the One-against-One SVM algorithm and a feature extraction method of Chinese questions to get high classification accuracy
Sponsorship	IEEE Comput. Soc. WIC ACM
Starting Page	334
Ending Page	337
File Size	200041
Page Count	4
File Format	PDF
ISBN	0769527477
DOI	10.1109/WI.2006.163
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-12-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine learning algorithms Feature Extraction Educational institutions Support Vector Machines Support vector machines Computer science Text categorization Support vector machine classification Cities and towns Search engines Question Classification Feature extraction Internet Semantic Dependency Relationship
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

Chinese Question Classification Based on Semantic Gram and SVM

An opinion mining approach for Romanian language

A Comparative Study on Vietnamese Text Classification Methods

Feature selection for text classification using OR+SVM-RFE

Machine Learning Techniques for Automated Web Page Classification Using URL Features

Chinese question classification in community question answering

Support Vector Data Description for image categorization from Internet images

PKIP: feature selection in text categorization for item banks

Multi-class SVM with negative data selection for Web page classification

Support Vector Machines for Text Categorization in Chinese Question Classification

Similar Documents

Chinese Question Classification Based on Semantic Gram and SVM

An opinion mining approach for Romanian language

A Comparative Study on Vietnamese Text Classification Methods

Feature selection for text classification using OR+SVM-RFE

Machine Learning Techniques for Automated Web Page Classification Using URL Features

Chinese question classification in community question answering

Support Vector Data Description for image categorization from Internet images

PKIP: feature selection in text categorization for item banks

Multi-class SVM with negative data selection for Web page classification

Support Vector Machines for Text Categorization in Chinese Question Classification