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语义文本相似度计算方法

韩程程 李磊 刘婷婷 高明

韩程程, 李磊, 刘婷婷, 高明. 语义文本相似度计算方法[J]. 华东师范大学学报(自然科学版), 2020, (5): 95-112. doi: 10.3969/j.issn.1000-5641.202091011
引用本文: 韩程程, 李磊, 刘婷婷, 高明. 语义文本相似度计算方法[J]. 华东师范大学学报(自然科学版), 2020, (5): 95-112. doi: 10.3969/j.issn.1000-5641.202091011
HAN Chengcheng, LI Lei, LIU Tingting, GAO Ming. Approaches for semantic textual similarity[J]. Journal of East China Normal University (Natural Sciences), 2020, (5): 95-112. doi: 10.3969/j.issn.1000-5641.202091011
Citation: HAN Chengcheng, LI Lei, LIU Tingting, GAO Ming. Approaches for semantic textual similarity[J]. Journal of East China Normal University (Natural Sciences), 2020, (5): 95-112. doi: 10.3969/j.issn.1000-5641.202091011

语义文本相似度计算方法

doi: 10.3969/j.issn.1000-5641.202091011
基金项目: 国家重点研发计划(2016YFB1000905); 国家自然科学基金(U1911203, U1811264, 61877018, 61672234, 61672384); 中央高校基本科研业务费专项资金; 上海市科技兴农推广项目(T20170303); 上海市核心数学与实践重点实验室资助项目(18dz2271000)
详细信息
    通讯作者:

    高 明, 男, 教授, 博士生导师, 研究方向为教育计算、知识图谱、知识工程、用户画像、社会网络挖掘、不确定数据管理. E-mail: mgao@dase.ecnu.edu.cn

  • 中图分类号: TP311

Approaches for semantic textual similarity

  • 摘要: 综述了语义文本相似度计算的最新研究进展, 主要包括基于字符串、基于统计、基于知识库和基于深度学习的方法. 针对每一类方法, 不仅介绍了其中典型的模型和方法, 而且深入探讨了各类方法的优缺点; 并对该领域的常用公开数据集和评估指标进行了整理, 最后讨论并总结了该领域未来可能的研究方向.
  • 图  1  语义文本相似度计算研究分类

    Fig.  1  Classification of semantic textual similarity

    图  2  监督学习方法模型架构

    Fig.  2  Model architecture for methods based on supervised learning

    表  1  基于字符串的语义文本相似度计算方法

    Tab.  1  String-based method for semantic textual similarity

    类型计算方法基本思想
    基于字符串编辑距离[11, 16]文本${ {S} }_{ {A} } $转换到文本 ${ {S} }_{ {B} } $所需的最少编辑操作次数. 编辑操作包括: 增、删、改.
    LCS[12]$\dfrac{ {2{{K} } } }{ { { {{L} }_{{A} } } + { {{L} }_{{B} } } } }$其中 K 表示${{S} }_{{A} }$${{S} }_{{B} }$的最长公共子序列的长度(可用动态规划求解).
    ${{L} }_{{A} }$${ {L} }_{ {B} }$分别表示${{S} }_{{A} }$$ {{S}}_{{B}} $的长度.
    N-Gram[13]$\dfrac{ {{N} }_{1} }{ {{N} }_{2} }$其中${{N} }_{1}$表示文本$ {{S}}_{{A}} $$ {{S}}_{{B}} $共有的 N 元组数量, $ {{N}}_{2} $表示总的 N 元组数量.
    Jaccard[15]$\dfrac{ {{A} }\bigcap {{B} } }{ {{A} }\bigcup {{B} } }$其中 AB 分别为表征$ {{S}}_{{A}} $$ {{S}}_{{B}} $的集合, 集合中的元素可以是字符、单词、N 元组等
    Dice系数$\dfrac{2\left|{{A} }\bigcap {{B} }\right|}{\left|{{A} }\right|+\left|{{B} }\right|}$其中 AB 分别表示文本$ {{S}}_{{A}} $$ {{S}}_{{B}} $的子集合, 分子表示 AB 交集个数的两倍,
    分母为 AB 集合中包含的元素个数之和
    下载: 导出CSV

    表  2  基于WordNet和同义词词林的计算方法

    Tab.  2  Methods based on WordNet and the synonymy thesaurus

    分类相关方法特点
    基于距离Rada等[32]、Richardon等[33]、Leacock等[34]
    Wu等[35]、Hirst等[36]、Yang等[37]
    利用概念结构树计算概念间距离, 并结合深度、
    层次信息计算语义文本相似度
    基于信息量Reshik[38]、Jiang等[39]、Lin等[40]通过概念包含的信息量进行语义文本相似度计算,
    信息量如何定义是该类方法改进的本质
    基于属性Lesk[41]、Banerjee等[42]、Pedersen等[43]利用概念的释义信息及类型信息计算语义文本相似度
    混合式Li等[44]、Bin等[45]、郑志蕴等[46]结合上述三类方法, 一般该类方法的算法复杂度较高
    下载: 导出CSV

    表  3  基于网络知识的计算方法

    Tab.  3  Methods based on network knowledge

    分类相关方法特点
    基于本体Strube等[50](WikiRelate!)将基于本体的方法(如基于距离和基于信息量的方法)
    迁移至维基百科上进行语义文本相似度计算
    基于向量空间Gabrilovich等[51](ESA)、Witten等[52]、Yeh等[53]、Camacho-Collados等[54](NASARI)将维基百科中的网页内容映射为高维向量,
    再通过基于向量空间的方法进行语义文本相似度计算
    下载: 导出CSV

    表  4  基于无监督学习的方法

    Tab.  4  Methods based on unsupervised learning

    分类相关方法特点
    基于自监督学习Doc2vec[62]、Sent2vec[63]、Skip-Thought[64]、Quick-Thought[65]
    SDAE[66]、FastSent[66]
    该类方法利用自监督学习设计相关任务, 通过数据本身携带的信息训练模型,
    然后通过训练好的模型得到句子的向量表示, 在此基础上计算语义文本相似度
    基于数学分析WMD[67]、SIF[68]、P-means[69]该类方法无须训练, 直接通过PCA降维、线性规划等数学工具, 将词向量
    加权求和得到句向量表示后, 计算向量间距离以表征语义文本相似度
    下载: 导出CSV

    表  5  监督方法模型简要总结

    Tab.  5  Summary of models based on supervised learning

    TypeModelsSentence encoder layerInteraction layerSimilarity measure layer
    Siamese NetworkDSSM[70]MLP-Cosine Similarity
    CNN-DSSM[71]CNN+MLP-Cosine Similarity
    LSTM-DSSM[72]LSTM-Cosine Similarity
    Siamese-LSTM[73]LSTM-Manhattan distance+ SVM
    Lin等[74]BiLSTM+ self-attention-dot product+ MLP
    Pontes等[75]CNN+LSTM-Manhattan distance
    InferSent[76]GRU、LSTM、BiGRU、BiLSTM等-Maxpooling+ MLP
    Yang等[77]DAN、Transformer-MLP
    USE[78]Transformer-MLP
    Interaction-basedABCNN[79]CNNAttentionPooling+ MLP
    PWIM[80]BiLSTMcosine similarity + Euclidean distance +dot productCNN+ MLP
    BiMPM[81]BiLSTMmatchingMLP
    DIIN[82]self-attentiondot productDenseNet
    DRCN[83]BiLSTM+DenseNet[84]vector addition+ vector subtraction+ vector moduloMLP
    下载: 导出CSV

    表  6  语义文本相似度任务常用公开数据集

    Tab.  6  Common datasets for semantic textual similarity

    数据集名称句子对数量(训练、测试)数据来源
    STS20125150(3150, 2000)现存释义集、新闻、视频描述、机器翻译评估集
    STS20133750(2000, 1750)新闻、机器翻译评估集
    STS201414974(7592, 7382)新闻、论坛、Twitter
    STS201514342(11342, 3000)新闻、论坛、图像描述
    STS20161884(1000, 884)新闻、机器翻译评估集、论坛
    STS20173210(2210, 1000)新闻、机器翻译评估集、论坛
    TwitterPPDB51524(42200, 9324)Twitter
    MSRVID1500(750, 750)视频描述
    SICK9927(5000, 4927)视频描述、图像描述
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-08-09
  • 网络出版日期:  2020-09-24
  • 刊出日期:  2020-09-24

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