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论文:2021,Vol:39,Issue(6):1387-1394 |
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引用本文： |
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谢金峰, 王羽, 葛唯益, 徐建. 基于多语义相似性的关系检测方法[J]. 西北工业大学学报 |
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XIE Jinfeng, WANG Yu, GE Weiyi, XU Jian. A relation detection method based on multi semantic similarity[J]. Northwestern polytechnical university |
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| 基于多语义相似性的关系检测方法 |
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| 谢金峰1,2, 王羽2, 葛唯益2, 徐建1 |
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1. 南京理工大学 计算机科学与工程学院, 江苏 南京 210094;
2. 中国电子科技集团公司第二十八研究所 信息系统工程重点实验室, 江苏 南京 210007 |
| 摘要: |
| 关系检测是知识库问答的关键步骤，直接影响问答质量。现有方法中基于编码比较的方法提取文本整体语义进行匹配会丢失序列的局部信息，而基于交互的方法在文本低层表征层面进行比较会忽略全局语义。针对现有方法无法兼顾全局语义和局部语义信息的问题，提出了一种基于多语义相似性的关系检测模型，通过BERT模型分别对问题和关系进行语义表示，然后引入注意力机制、双向长短期记忆网络和多层感知机进行局部关联性分析；利用BERT计算出的句向量中含有序列的全局语义信息，设计了问题和关系句向量的全局相似度度量。在基准数据集SimpleQuestions和WebQSP上进行了实验验证，所提方法分别取得了93.92%和87.81%的准确率，优于其他现有的方法。 |
| 关键词:
关系检测
语义相似性
双向长短期记忆网络
注意力机制
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| A relation detection method based on multi semantic similarity |
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| XIE Jinfeng1,2, WANG Yu2, GE Weiyi2, XU Jian1 |
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1. School of Computer Science & Engineering, Nanjing University of Science & Technology, Nanjing 210094, China;
2. Science and Technology on Information Systems Engineering Laboratory, the 28th Research Institute of China Electronics Technology Group Corporation, Nanjing 210007, China |
| Abstract: |
| Relation detection is a critical step of knowledge base question answering, which directly affects the quality of question answering. Among the existing methods, the encoding-comparison method extracts text global semantic information for matching, which often ignores the local semantic feature of text sequence. The interaction approach performs the comparison on low-level representations based on the sequence local information, which fails to consider the global semantic information of the input sequences. To solve the issues, this paper proposes a relation detection model based on Bert model and multi-semantic similarity considering global and local semantic information. First, our model introduces Bert as a text encoding layer to represent questions and relations as sequences of vectors. And then, a bi-directional long short-term memory (Bi-LSTM) layer with the attention mechanism is used to analyze the local semantic relevance and calculate the local similarity. Finally, our model uses a distance calculation formula to measure the global semantic relevance between questions and relations. The experimental results on two benchmark datasets, SimpleQuestions and WebQSP, show that the proposed model achieves the accuracy of 93.92% and 87.81% respectively, performs better than state-of-the-art approaches. |
| Key words:
relation detection
semantic similarity
Bi-LSTM
attention mechanism
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| 收稿日期: 2021-03-20
修回日期:
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| DOI: 10.1051/jnwpu/20213961387 |
| 基金项目: 国家自然科学基金（61872186）与信息系统工程重点实验室开放基金（05201901）资助 |
| 通讯作者: 徐建(1979-),南京理工大学教授,主要从事数据挖掘及知识图谱研究。e-mail:dolphin.xu@njust.edu.cn
Email：dolphin.xu@njust.edu.cn |
| 作者简介: 谢金峰(1998-),南京理工大学硕士研究生,主要从事自然语言处理及深度学习研究。
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| 作者相关文章 |
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| 谢金峰 在本刊中的所有文章 |
| 王羽 在本刊中的所有文章 |
| 葛唯益 在本刊中的所有文章 |
| 徐建 在本刊中的所有文章 |
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