DocumentCode :
1938341
Title :
Research on Chinese FAQ Question Answering System in Restricted Domain
Author :
Yu, Zheng-tao ; Qiu, Yan-xia ; Deng, Jin-Hui ; Han, Lu ; Mao, Cun-li ; Meng, Xiang-yan
Author_Institution :
Kunming Univ. of Sci. & Technol., Kunming
Volume :
7
fYear :
2007
fDate :
19-22 Aug. 2007
Firstpage :
3927
Lastpage :
3932
Abstract :
FAQ (frequently-asked question) is a good question and answer model to realize business advisory system in restricted domain. A FAQ question answering system model is presented in this paper. With the help of the idea of ontology, a knowledge base is constructed in the domain. With the help of language KDML (Knowledge Database Mark-up Language) of HowNet,the domain ontology and the relationship of it are defined and described, and the fusion of domain knowledge base (domain HowNet) and common knowledge base(HowNet) is realized. On this basis, a question similarity calculation method, which makes use of the characteristics of the domain question and combines lexical relationship, syntactic interdependent relationship and the semantic relationship of domains among question sentences, is implemented. And based on the question similarity calculation, retrieval of related question from the candidate question set and extraction of answers can be implemented with this method. The result of Yunnan tourism question-answer model experiment shows that this method is feasible and effective.
Keywords :
information retrieval; knowledge based systems; natural languages; ontologies (artificial intelligence); Chinese FAQ question answering system; KDML; Knowledge Database Mark-up Language; Yunnan tourism question-answer model; business advisory system; domain HowNet; domain ontology; frequently-asked question; Application software; Automation; Computer applications; Cybernetics; Databases; Information processing; Learning systems; Machine learning; Natural languages; Ontologies; Domain ontology; Frequently-asked question; Question similarity; Restricted domain question answering system;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2007 International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-0973-0
Electronic_ISBN :
978-1-4244-0973-0
Type :
conf
DOI :
10.1109/ICMLC.2007.4370832
Filename :
4370832
Link To Document :
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