DocumentCode :
545387
Title :
Study on question classification approach mixing multiple semantic characteristics together
Author :
Duan, LiGuo ; Niu, YanQin ; Chen, Junjie
Author_Institution :
Coll. of Comput. Sci. & Technol., Taiyuan Univ. of Technol., Taiyuan, China
Volume :
1
fYear :
2011
fDate :
11-13 March 2011
Firstpage :
354
Lastpage :
357
Abstract :
This article proposes such a question classification approach that integrates multiple semantic features. It is aimed at these two questions in Chinese question classification models: inaccurate semantic information extraction and too slow processing speed caused by too high Eigenvector dimension. With the help of HowNet and the support vector machine and syntactic and semantic information of question sentences taken into consideration, this method picks up four classification features - the interrogative, the main sememe of key words, the named entity and the singular/plural form of nouns - to classify factual question sentences. Within the process of sememe extraction, the meaning disambiguation technology is added in. Algorithms to combine above characteristics and produce a better result are presented and justified. Experiment of this method has been done in the Chinese question classification set of the information retrieval laboratory of HarBin Institute of Technology, and results show that the method with multiple integrated semantic features is better than that with single feature.
Keywords :
eigenvalues and eigenfunctions; feature extraction; pattern classification; support vector machines; text analysis; word processing; Chinese question classification model; HowNet; eigenvector dimension; factual question sentence classification; keywords; multiple semantic feature integration; semantic information extraction; sememe extraction; support vector machine; syntactic information; Accuracy; Classification algorithms; Data mining; Feature extraction; Semantics; Support vector machine classification; Interrogative; Named Entities; Question Classification; Sememe; Support Vector Machine;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Research and Development (ICCRD), 2011 3rd International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-61284-839-6
Type :
conf
DOI :
10.1109/ICCRD.2011.5764035
Filename :
5764035
Link To Document :
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