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基于量子理论的句子相似度研究

孟冰卿 马雷

孟冰卿, 马雷. 基于量子理论的句子相似度研究[J]. 华东师范大学学报(自然科学版), 2021, (1): 60-66. doi: 10.3969/j.issn.1000-5641.201922018
引用本文: 孟冰卿, 马雷. 基于量子理论的句子相似度研究[J]. 华东师范大学学报(自然科学版), 2021, (1): 60-66. doi: 10.3969/j.issn.1000-5641.201922018
MENG Bingqing, MA Lei. Study on sentence similarity based on quantum theory[J]. Journal of East China Normal University (Natural Sciences), 2021, (1): 60-66. doi: 10.3969/j.issn.1000-5641.201922018
Citation: MENG Bingqing, MA Lei. Study on sentence similarity based on quantum theory[J]. Journal of East China Normal University (Natural Sciences), 2021, (1): 60-66. doi: 10.3969/j.issn.1000-5641.201922018

基于量子理论的句子相似度研究

doi: 10.3969/j.issn.1000-5641.201922018
详细信息
    通讯作者:

    马 雷, 男, 副教授, 硕士生导师, 研究方向为量子信息与量子计算. E-mail: lma@phy.ecnu.edu.cn

  • 中图分类号: O413.2

Study on sentence similarity based on quantum theory

  • 摘要: 量子理论所具备的叠加、纠缠、不相容和干涉等特征使其成为优秀的建模框架. 研究了量子理论在自然语言理解方面的应用潜力. 在自然语言句子匹配任务上, 探讨了量子理论作为一种形式化框架捕捉句子、词语的语义和语义建模上的能力: 利用量子态构建句子的语义Hilbert空间, 计算句子信息变换过程中信息的保真度; 与此同时, 将量子理论与Word Embedding技术巧妙结合, 在高维低秩向量空间中表示单词或概念, 求取句子的相似性. 在一个真实业务场景中构造的问句匹配数据集上, 模拟数据表明, 所提出的方法相比于经典方法取得了更好的效果, 为以后进行多个句子的相似度研究提供了新的思路, 也是计算机科学与量子理论学科交叉研究领域的一个突破, 符合当下研究的方向.
  • 图  1  自然语言句子的不同词性组成

    Fig.  1  Different parts of speech composition in natural language sentences

    图  2  不同词性的单词在模型中所占权重取值分析

    Fig.  2  Analysis of the weights of words from different parts of speech in the model

    表  1  实验数据示例

    Tab.  1  Examples from simulation data

    句1句2相似度标签
    How does the bank modify the repayment?How to change the repayment card?1
    When can I use the particle loan?When can I borrow money from the particles?1
    Does WeChat count?How much is left?0
    Will prepayment interest be reduced?How to clear the certificate?0
    下载: 导出CSV

    表  2  各类词性权重值取值

    Tab.  2  Weight assigned to various parts of speech

    动词类名词类修饰类其他
    1.021.010.950.92
    下载: 导出CSV

    表  3  测试集相似度计算结果比较

    Tab.  3  Comparison of test set similarity calculations

    模型A/(%)P/(%)R/(%)F1/(%)
    Jaccard_similarity65.1563.0664.3963.72
    Cosine_similarity_TF63.1363.3762.4262.89
    Cosine_similarity_TFIDF63.1163.3462.4862.91
    本文方法65.9363.8173.5068.31
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-11-21
  • 刊出日期:  2021-01-27

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