NDLI: An integrated knowledge-based and machine learning approach for Chinese question classification

Content Provider	IEEE Xplore Digital Library
Author	Min-Yuh Day Cheng-Wei Lee Shih-Hung Wu Chorng-Shyong Ong Wen-Lian Hsu
Copyright Year	2005
Description	Author affiliation: Inst. of Inf. Sci., Acad. Sinica, Fuzhou, China (Min-Yuh Day; Cheng-Wei Lee)
Abstract	Question classification plays an important role in question-answering systems. Chinese question classification is the process that analyzes a question and labels it based on its question type and expected answer type. In this paper, we propose an integrated knowledge-based and machine learning approach for Chinese question classification that focuses on factoid question answering. We develop a Chinese question classification scheme for CLQA C-C (cross language question answering Chinese to Chinese) factoid question answering, and define a coarse-grained and fine-grained classification taxonomy for a Chinese question-answering system. We adopt INFOMAP inference engine to support the knowledge-based approach for Chinese questions, which can be formulated as templates and use SVM (support vector machines) as the machine learning approach for large collections of labeled Chinese questions. Our experimental results show that the accuracy of Chinese question classification using INFOMAP alone is 88% and 73.5% with SVM alone. In contrast, classification based on a hybrid approach that incorporates SVM and INFOMAP yields an accuracy rate of 92%.
Starting Page	620
Ending Page	625
File Size	2742546
Page Count	6
File Format	PDF
ISBN	0780393619
DOI	10.1109/NLPKE.2005.1598811
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-30
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Computer science Chaos Information science Taxonomy Natural languages Support vector machine classification Machine learning Information management Engines
Content Type	Text
Resource Type	Article

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