TY - GEN
T1 - UB-CQA
T2 - 12th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2017
AU - Zhu, Ziye
AU - Liu, Xiaoqian
AU - Li, Huakang
AU - Li, Tao
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - Intelligent Community Question Answering (CQA) system is a popular research topic in Natural Language Processing (NLP) area. Recently, the development of distributed data mining has significantly improved the efficiency of the searching and sorting operations in CQA database. However, problems are left unanswered such as how to choose the best answer among multiple candidates for a single question. To solve this issue, we design a CQA system called UB-CQA. First, in database refinement part, we construct an optimized and structured database by the best answer choosing method leveraging user attribute provided by the response provider. Second, in human-computer part, the UB-CQA is able to search and provide a more satisfying answer to users by similarity calculation and re-ranking method leveraging text categorization information. Empirical evaluations show that the best answer choosing and the candidate question re-ranking methods bring great improvements in accuracy and reliability.
AB - Intelligent Community Question Answering (CQA) system is a popular research topic in Natural Language Processing (NLP) area. Recently, the development of distributed data mining has significantly improved the efficiency of the searching and sorting operations in CQA database. However, problems are left unanswered such as how to choose the best answer among multiple candidates for a single question. To solve this issue, we design a CQA system called UB-CQA. First, in database refinement part, we construct an optimized and structured database by the best answer choosing method leveraging user attribute provided by the response provider. Second, in human-computer part, the UB-CQA is able to search and provide a more satisfying answer to users by similarity calculation and re-ranking method leveraging text categorization information. Empirical evaluations show that the best answer choosing and the candidate question re-ranking methods bring great improvements in accuracy and reliability.
KW - community question answering system
KW - quality prediction
KW - text categorization
KW - text feature extraction
KW - user attribute
UR - http://www.scopus.com/inward/record.url?scp=85048105033&partnerID=8YFLogxK
U2 - 10.1109/ISKE.2017.8258750
DO - 10.1109/ISKE.2017.8258750
M3 - Conference Proceeding
AN - SCOPUS:85048105033
T3 - Proceedings of the 2017 12th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2017
SP - 1
EP - 9
BT - Proceedings of the 2017 12th International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2017
A2 - Li, Tianrui
A2 - Lopez, Luis Martinez
A2 - Li, Yun
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 24 November 2017 through 26 November 2017
ER -