[1],,,.֪ʶĿʴϵͳ[J].ϵͳѧ,2018,13(4):557-563.[doi:10.11992/tis.201707039]
ZHANG Tao,JIA Zhen,LI Tianrui,et al.Open-domain question-answering system based on large-scale knowledge base[J].CAAI Transactions on Intelligent Systems,2018,13(4):557-563.[doi:10.11992/tis.201707039]
ϵͳѧ[ISSN 1673-4785/CN 23-1538/TP] :
13
:
20184
ҳ:
557-563
Ŀ:
ѧġ֪ʶ
:
2018-07-05
- Title:
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Open-domain question-answering system based on large-scale knowledge base
- :
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, , ,
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Ͻͨѧ Ϣѧ뼼ѧԺ, Ĵ ɶ 611756
- Author(s):
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ZHANG Tao, JIA Zhen, LI Tianrui, HUANG Yanyong
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School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China
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- ؼ:
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ʴϵͳ; ; ʵʶ; ʵ; ֪ʶ
- Keywords:
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question-answering system; open domain; entity recognition; entity linking; knowledge base
- :
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TP391.1
- DOI:
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10.11992/tis.201707039
- ժҪ:
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ʴϵͳܹû⣬ֱӷش𰸡ʴϵͳģܻشض⡣˻ڴģ֪ʶĿʴϵͳʵַϵͳȲԶʵִʺCRFģϵķʶʾе壻Σģƥ䷽ʾе֪ʶʵ彨ӣȻͨƶȼԼƥȶַʶʾеνʲ֪ʶʵԽʵʹ𰸻ȡϵͳƽF-MeasureֵΪ0.695 6᷽ڻ֪ʶĿʴϾпԡ
- Abstract:
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Question-answering (QA) systems can understand user questions and return answers directly. Currently, most QA systems can only answer questions pertaining to specific domains. In this paper, we propose a method for constructing an open-domain QA system based on a large-scale knowledge base. First, we present an approach based on a visual dictionary and a conditional random field (CRF) model to identify the subject in question. Next, we use a fuzzy matching method to link the entity in question to that in the knowledge base, and apply similarity computation and rule matching methods to recognize the question predicates and link them to the attributes of the knowledge entity. Lastly, we implement entity disambiguation and answer retrieval. The mean F-measure value of the proposed system is 0.695 6, which indicates the feasibility of the proposed method for an open-domain QA system for a large-scale knowledge base.
ע/Memo
ո:2017-07-25
Ŀ:ȻѧĿ61573292ȻѧѧĿ61603313.
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ͨѶ:.E-mail:tzhangswjtu@163.com.
/Last Update:
2018-08-25