问答系统命名实体识别改进方法研究
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1.常州工学院电气信息工程学院,常州,213022;2.河海大学物联网工程学院,常州,213022;3.江苏省特种机器人与智能技术重点实验室,常州,213022

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江苏省重点研发计划(BK20192004, BE2018004-04)资助项目;中央高校科研基本业务费(B200202205)资助项目;飞行交通管理与技术重点实验室开放课题(SKLATM201901)资助项目。


Research on the Improved Method of Named Entity Recognition in Q & A System
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Affiliation:

1.School of Electrical and Information Engineering, Changzhou Institute of Technology, Changzhou, 213022, China;2.School of IoT Engineering, Hohai University,Changzhou,213022, China;3.Changzhou Key Laboratory of Robotics and Intelligent Technology,Changzhou,213022,China

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    摘要:

    问答系统是一种以准确且自然的语言来回答用户提问的系统。本文对其中涉及的“命名实体识别”环节尝试了一些改进措施:针对传统单向模板匹配耗时耗力的问题,提出一种双向格子结构的长短时记忆网络(Lattice Bi-LSTM),解决了命名实体识别中对句子处理不当和对分词结果具有依赖性的两大问题。与单向结构相比,双向结构能更好地利用句子信息,使输出结果更具鲁棒性,从而更准确地捕获语义信息;针对传统方法未考虑实体间相似度的非线性耦合性问题,提出一种利用周期性核函数将“相似”实体准确链接到知识库中去的方法。对提出的两种改进方法进行了实验验证,结果表明:与经典方法相比,所用方法具有显著的改进效果。

    Abstract:

    The Q & A system is a kind of system which can answer user’s questions with accurate and natural language. Some improvement measures have been tried for “named entity recognition”. Aiming at the time- and labor-consuming problem of traditional one-way template matching, this paper proposes a lattice bi-directional structure of long short-term memory (Lattice Bi-LSTM)network, which solves the problems of improper sentence processing and dependence on the result of word segmentation in named entity recognition. Compared with the unidirectional structure, the bi-directional structure can make better use of sentence information and make the output more robust, thus capturing semantic information more accurately. To solve the problem of non-linear coupling of similarity between entities in traditional methods, a method is proposed to link “similar” entities to the knowledge base accurately by using periodic kernel function. The two improved methods are verified by experiments, whose results show that they have significant improvement effects compared with the classical method.

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鲍静益,于佳卉,徐宁,姚潇,刘小峰.问答系统命名实体识别改进方法研究[J].数据采集与处理,2020,35(5):930-941

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  • 收稿日期:2020-07-07
  • 最后修改日期:2020-08-25
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  • 在线发布日期: 2020-10-22