| 一种基于Spark的论文相似性快速检测方法 |
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| 引用本文： | 卓可秋,童国平,虞为.一种基于Spark的论文相似性快速检测方法[J].图书情报工作,2015,59(11):134-142. |
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| 作者姓名： | 卓可秋 童国平 虞为 |
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| 作者单位： | 1. 南京大学信息管理学院, 南京, 210023;
2. 江苏省数据工程与知识服务重点实验室(南京大学), 南京, 210023 |
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| 基金项目： | 本文系国家社会科学基金重大项目“面向突发事件应急决策的快速响应情报体系研究”(项目编号:13&ZD174)、国家社会科学基金项目“基于关联数据的图书馆语义云服务研究”(项目编号:12CTQ009)、江苏省社会科学项目青年项目“基于语义云服务的数字阅读推广研究”(项目编号:14TQC003)、中央高校基本科研业务费专项资金资助项目“基于用户的标语用分析的社会化标签知识组织研究”(项目编号:1435003)和江苏省高校自然科学研究面上资助项目“基于语义消歧技术的社会化标签知识组织研究”(项目编号:15KJB520013)研究成果之一。 |
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| 摘 要： | 目的/意义] , 从大规模已知文本集中检测出与待检测论文的相似文本并计算相似度大小, 用于满足在线论文相似性检测秒级响应需求。方法/过程] 采用分治法策略, 对已知文本句集进行基于正交基的软聚类预处理, 并对软聚类后的每个簇建立倒排索引。接着在快数据处理平台Spark上执行相似性检测, 采用字符结合词组形式计算出待检测论文与已知文本的相似度大小。结果/结论] 通过200万规模的已知文本集实验结果显示, 综合4种类型的待检测论文, 所提出的倒排索引结合软聚类算法准确率P为100.0%, 召回率R为93.6%, 调和平均值F为96.7%。调和平均值F比相似性检测算法LCS高10%左右, 比Simhash算法高约23%。在检测速度上, 对于一篇字数为5 000左右的待检测论文, 检测时间约为6.5秒, 比Simhash算法快近300倍, 比LCS算法快约4 000倍。此外, 实验结果还表明基于Spark的分布式并行相似性检测算法具有较好的可扩展性。
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| 关 键 词： | 论文相似性检测 Spark快数据处理 正交基软聚类 倒排索引 |
| 收稿时间： | 2015-05-10 |
An Approach for Scientific Paper Similarity Rapid Detection Based on Spark |
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| Zhuo Keqiu,Tong Guoping,Yu Wei.An Approach for Scientific Paper Similarity Rapid Detection Based on Spark[J].Library and Information Service,2015,59(11):134-142. |
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| Authors: | Zhuo Keqiu Tong Guoping Yu Wei |
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| Institution: | 1. School of Information Management, Nanjing University, Nanjing 210023;
2. Jiangsu Key Laboratory of Data Engineering and Knowledge Service(Nanjing University), Nanjing 210023 |
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| Abstract: | Purpose/significance] This paper detects the texts similar with papers to be detected from the large scale known texts and computes their similarities, to meet the second response requirement of online paper similarity detection. Method/process] It uses divide and conquer strategy to softly cluster known text sentence set, and establishes inverted index for each cluster after soft clustering. Then it performs the similarity computing between papers to be detected and known texts on the fast data processing platform-Spark, using the method of character combined with phrase. Result/conclusion] Through the experiment of two million known texts set, the results show that the proposed inverted index algorithm combined with soft clustering has precision rate P 100.0%, recall rate R 93.6% and harmonic mean F value 96.7%, integrating four types of papers to be detected. The harmonic mean F is about 10% higher than LCS algorithm and 23% higher than Simhash algorithm. In the detection of the paper with 5 000 words, the proposed algorithm has the detection time of about 6.5 seconds, nearly 300 times faster than the Simhash algorithm, and approximately 4 000 times faster than LCS algorithm. In addition, the results also show that the Spark based distributed parallel similarity detection algorithm has better scalability. |
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| Keywords: | paper similarity detection Spark fast data processing orthogonal soft clustering inverted index |
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