Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (3): 1-7.doi: 10.6040/j.issn.1672-3961.0.2019.417

• Machine Learning & Data Mining •     Next Articles

Semantic relation recognition for natural language question answering

Jiangli DUAN1,2(),Xin HU2,*()   

  1. 1. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
    2. College of Big Data and Intelligent Engineering, Yangtze Normal University, Chongqing 408100, China
  • Received:2019-07-22 Online:2020-06-20 Published:2020-06-16
  • Contact: Xin HU E-mail:d180201004@stu.cqupt.edu.cn;huxin@yznu.edu.cn
  • Supported by:
    重庆邮电大学博士研究生人才培养项目(BYJS201908);重庆市教委科技研究计划青年项目(KJQN201901414);长江师范学院高层次人才科研启动金项目(0107/011160052)

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Abstract:

To avoid the deficiency of excessive dependence on named entity recognition during the understanding perio, logic relationships among vital information in Chinese natural language question were understood by semantic relation. An algorithm for recognizing semantic relations based on dependency structures was proposed, which first recognized three kinds of valuable dependency structures that were vital for obtaining semantic relations, and then combined or transformed these dependency structures to obtain semantic relations. The effectiveness and scalability of the proposed method were verified by extensive experiments over Chinese benchmark question answering datasets, and the experiments results showed that this method could also understand Chinese natural language questions when recognition of named entity failed.

Key words: knowledge graph, natural language question answering, semantic relation, dependency structure

CLC Number: 

  • TP391

Fig.1

Dependency structures and semantic relations"

Fig.2

Dependency structures and named entity"

Table 1

Three kinds of dependency structures"

分类 依赖结构集 备注
δsubject-predicate nsubj, nsubjpass, xsubj 主-谓依赖结构
δpredicate-object dobj 谓-宾依赖结构
δnmod nmod 谓-宾或主-宾依赖结构

Table 2

Results for combining S-P/P-O/nmod"

数据集 视角 Precision Recall F-measure
NLPCC2018 宏观 0.65 0.52 0.58
微观 0.45 0.52 0.47
CCKS2018 宏观 0.67 0.60 0.62
微观 0.58 0.57 0.58

Table 3

Comparative analysis of semantic relations recognition results in questions"

数据集 问句数量 正确识别语义关系的问句数量
本研究方法 其他方法[1-11]
NLPCC2018 122 61 0
CCKS2018 86 48 0

Table 4

Results for questions in named entity recognitionon empty question set"

数据集 视角 Precision Recall F-measure
NLPCC2018(61/122) 宏观 0.62 0.52 0.55
微观 0.45 0.50 0.47
CCKS2018(48/86) 宏观 0.62 0.49 0.58
微观 0.58 0.60 0.55

Table 5

Results for only combining S-P/P-O"

数据集 视角 Precision Recall F-measure
NLPCC2018 宏观 0.85 0.22 0.35
微观 0.22 0.22 0.22
CCKS2018 宏观 0.67 0.18 0.28
微观 0.17 0.18 0.17

Fig.3

Precision with increase of the number of questions"

Fig.4

Recall with increase of the number of questions"

Fig.5

F-measure with increase of the number of questions"

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