NDLI: Latent semantic analysis for question classification with neural networks

Content Provider	IEEE Xplore Digital Library
Author	Loni, B. Khoshnevis, S.H. Wiggers, P.
Copyright Year	2011
Description	Author affiliation: Department of Media and Knowledge Engineering, Delft University of Technology, PO Box 5031, 2600 GA, Netherlands (Loni, B.; Khoshnevis, S.H.; Wiggers, P.)
Abstract	An important component of question answering systems is question classification. The task of question classification is to predict the entity type of the answer of a natural language question. Question classification is typically done using machine learning techniques. Most approaches use features based on word unigrams which leads to large feature space. In this work we applied Latent Semantic Analysis (LSA) technique to reduce the large feature space of questions to a much smaller and efficient feature space. We used two different classifiers: Back-Propagation Neural Networks (BPNN) and Support Vector Machines (SVM). We found that applying LSA on question classification can not only make the question classification more time efficient, but it also improves the classification accuracy by removing the redundant features. Furthermore, we discovered that when the original feature space is compact and efficient, its reduced space performs better than a large feature space with a rich set of features. In addition, we found that in the reduced feature space, BPNN performs better than SVMs which are widely used in question classification. Our result on the well known UIUC dataset is competitive with the state-of-the-art in this field, even though we used much smaller feature spaces.
Starting Page	437
Ending Page	442
File Size	334118
Page Count	6
File Format	PDF
ISBN	9781467303651
e-ISBN	9781467303675
e-ISBN	9781467303668
DOI	10.1109/ASRU.2011.6163971
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-11
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Feature extraction Accuracy Vectors Neurons Semantics Kernel
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
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