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数据分析与知识发现  2017, Vol. 1 Issue (6): 1-11     https://doi.org/10.11925/infotech.2096-3467.2017.06.01
  综述评介 本期目录 | 过刊浏览 | 高级检索 |
文本相似度计算方法研究综述
陈二静1,2(), 姜恩波1
1中国科学院成都文献情报中心 成都 610041
2中国科学院大学 北京 100049
Review of Studies on Text Similarity Measures
Chen Erjing1,2(), Jiang Enbo1
1Chengdu Documentation and Information Center, Chinese Academy of Sciences, Chengdu 610041, China
2University of Chinese Academy of Sciences, Beijing 100049, China
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摘要 

目的】分析文本相似度计算方法, 了解该领域的发展态势。【文献范围】在CNKI和Web of Science中分别以检索式“篇名: 文本相似度 OR篇名: 词汇相似度 OR篇名: 语义相似度”和“TI: ‘text similarity’ or ‘semantic similarity’ or ‘lexical similarity’ ”并限定文献类型进行检索, 最终得到69篇重点文献。【方法】对文本相似度计算方法进行系统梳理, 分析重点方法的基本思想、特点并总结未来发展方向。【结果】形成了较为全面的分类描述体系, 文本相似度计算方法可分为4类: 基于字符串的方法、基于语料库的方法、基于世界知识的方法和其他方法。其中, 基于神经网络和基于世界知识的方法以及针对跨领域文本的相似度计算将成为该领域的发展趋势。【局限】仅将不同方法本身作为探讨的核心, 未进一步分析方法的应用情况。【结论】有助于全面把握和深入了解文本相似度计算方法的研究现状和未来趋势。

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陈二静
姜恩波
关键词 文本相似度语义相似度本体词袋模型神经网络    
Abstract

[Objective] This paper analyzes the popular text similarity measures and discusses their latest developments. [Coverage] We retrieved 69 key articles from CNKI and Web of Science databases by searching “TI: ‘text similarity’ or ‘semantic similarity’ or ‘lexical similarity’ ” in Chinese and English respectively. [Methods] We systematically reviewed the text similarity measures focusing on their basic concepts, characteristics and future directions. [Results] There were four types of text similarity measures: String-based, Corpus-based, Knowledge-based and others. Measures based on the neural network, Knowledge-based measures and inter-disciplinary measures could be the future research directions. [Limitations] We did not discuss the applications of those measures. [Conclusions] This paper is a comprehensive review of text similarity measure research.

Key wordsText Similarity    Semantic Similarity    Ontology    Bag of Words Model    Neural Network
收稿日期: 2017-05-09      出版日期: 2017-08-25
ZTFLH:  TP391 G35  
引用本文:   
陈二静, 姜恩波. 文本相似度计算方法研究综述[J]. 数据分析与知识发现, 2017, 1(6): 1-11.
Chen Erjing,Jiang Enbo. Review of Studies on Text Similarity Measures. Data Analysis and Knowledge Discovery, 2017, 1(6): 1-11.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2017.06.01      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2017/V1/I6/1
  文本相似度计算方法分类
类型 方法 基本思想 类型 特点与不足
基于字符 编辑距离 SA转换到SB需要删除、插入、替换操作的最少次数。 字符组成 计算准确, 但费时。
汉明距离[13] $1-\left( \sum\limits_{k=1}^{n}{{{x}_{k}}\oplus {{y}_{k}}} \right)/n$, 其中xk, yk分别表示字符串SASB
对应码字第K位的分量。
字符组成 采用模2加运算, 简化长文本计算,
效率高。
LCS 共现且最长的子字符串。 字符顺序 原理简单, 针对派生词和短文本有较好效果, 但不适用于长文本。
Jaro-Winkler ${{d}_{j}}=\frac{1}{3}\left( \frac{m}{|{{S}_{A}}|}+\frac{m}{|{{S}_{B}}|}+\frac{m-t}{m} \right)$, 其中m是匹配的字符数;
t是换位的数目。相似度计算公式为${{d}_{j}}+(lp(1-{{d}_{j}}))$, 其中dj是两个字符串的Jaro 距离, l是前缀相同的长度, 规定最大为4。Winkler将p定义为0.1。
字符顺序 考虑了前缀相同的重要性, 针对短
文本有较好效果, 但不适用于长文本。
N-gram $\frac{n}{n}$ 集合思想 n可调, 方法较为灵活, 但不适用于长文本。
基于词语 余弦相似度 $\frac{\overrightarrow{{{S}_{A}}}\cdot \overrightarrow{{{S}_{B}}}}{||{{S}_{A}}||\ ||{{S}_{B}}||}$ 词语组成 将文本置于向量空间, 解释性强, 较为常用, 但不适用于长文本。
Dice系数[14] $\frac{2\times comm({{S}_{A}},{{S}_{B}})}{leng({{S}_{A}})+leng({{S}_{B}})}$ 词语组成 增强相同部分的作用, 有效关注较短的相同文本。
欧式距离 $\sqrt{S_{A}^{2}+S_{B}^{2}}$ 词语组成 算法简单直接, 但效果粗糙, 不适用于长文本。
Jaccard $\frac{{{S}_{A}}\ \bigcap {{S}_{B}}}{{{S}_{A}}\ \bigcup {{S}_{B}}}$ 集合思想 不适用于长文本。
Overlap Coefficient $\frac{{{S}_{A}}\ \bigcap {{S}_{B}}}{\min ({{S}_{A}},{{S}_{B}})}$ 集合思想 当一个字符串是另一个字符串的子字符串时, 相似度最大。
  基于字符串的代表方法
基于距离 基于内容 基于属性 混合式
基本
原理
用概念之间的路径长度表示
语义距离
用概念词共享的信息量化它们之间的语义相似度 用概念词之间的公共属性数
量衡量它们之间的相似度
将基于距离、基于内容、基于属性三种方法综合计算概念之间的相似度
代表
方法
Shortest Path[38]、Wu等[39]
Weighted Links[40]、Li等[41]
刘群等[10]
Lin[42]、Resnik[43]、Lord等[44]、边振兴[45] Tversky[46] 葛斌等[47]、王艳娜等[48]、李文清等[49]
特点 在计算方法中加入了节点深度、密度、强度、宽度及分类体系
层次等影响因子
计算方法采用不同节点的信息量以及表达信息内容的不同公式 计算效果依赖于本体属性集的完整性 计算方法中权重参数设置大多依赖领域专家
  基于本体的方法
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