NDLI: An Effective Feature-Weighting Model for Question Classification

Content Provider	IEEE Xplore Digital Library
Author	Huang, Peng Bu, Jiajun Chen, Chun Qiu, Guang
Copyright Year	2007
Abstract	Question classification is one of the most important sub- tasks in Question Answering systems. Now question tax- onomy is getting larger and more fine-grained for better answer generation. Many approaches to question classifi- cation have been proposed and achieve reasonable results. However, all previous approaches use certain learning al- gorithm to learn a classifier from binary feature vectors, extracted from small size of labeled examples. In this pa- per we propose a feature-weighting model which assigns different weights to features instead of simple binary val- ues. The main characteristic of this model is assigning more reasonable weight to features: these weights can be used to differentiate features each other according to their contri- bution to question classification. Furthermore, features are weighted depending on not only small labeled question col- lection but also large unlabeled question collection. Exper- imental results show that with this new feature-weighting model the SVM-based classifier outperforms the one with- out it to some extent.
Starting Page	32
Ending Page	36
File Size	1257452
Page Count	5
File Format	PDF
ISBN	0769530729
e-ISBN	9780769530727
DOI	10.1109/CIS.2007.12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-12-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Taxonomy Moon Natural languages Search engines Feature extraction Educational institutions Natural language processing Computer security Computational intelligence
Content Type	Text
Resource Type	Article

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