NDLI: Two Level Question Classification Based on SVM and Question Semantic Similarity

Content Provider	IEEE Xplore Digital Library
Author	Jibin Fu Youli Qu Zhifei Wang
Copyright Year	2009
Abstract	Question classification is very important in question answering system. This paper presents our research about question classification in a real-world on-line interactive question answering system in computer service & support domain. In the domain, questions are divided into 15 cursory categories and 220 sub-categories. The difference of this system is that standard question sentences represent the subcategories rather than only classification criterion. For the special situation, the two level question classification method is present in the paper. Support Vector Machine method is adopted to train a classifier on coarse categories; question semantic similarity model is used to classify the question into sub-categories. The lexical feature and domain ontology concept hierarchy is constructed and exploited to enhance the expression capacity of the feature characteristic for both feature selection for SVM and question semantic similarity computing. When trained and tested on the 11000 question instances in the domain, our approach reaches an accuracy up to 91.5%, which outperforms the result of the baseline.
Starting Page	366
Ending Page	370
File Size	328694
Page Count	5
File Format	PDF
ISBN	9780769535593
DOI	10.1109/ICECT.2009.67
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-02-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	question classification Ontologies Mathematics SVM Application software Information technology Intelligent robots Support vector machines Computer science question semantic similarity Support vector machine classification Machine learning Testing
Content Type	Text
Resource Type	Article

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