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CLC number: TP391.3

On-line Access: 2017-12-04

Received: 2016-06-17

Revision Accepted: 2016-12-12

Crosschecked: 2017-11-01

Cited: 0

Clicked: 1842

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Lei-lei Kong

http://orcid.org/0000-0002-4636-3507

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Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.10 P.1556-1572

http://doi.org/10.1631/FITEE.1601344


A machine learning approach to query generation in plagiarism source retrieval


Author(s):  Lei-lei Kong, Zhi-mao Lu, Hao-liang Qi, Zhong-yuan Han

Affiliation(s):  College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China; more

Corresponding email(s):   kongleilei1979@gmail.com, haoliang.qi@gmail.com

Key Words:  Plagiarism detection, Source retrieval, Query generation, Machine learning, Learning to rank


Lei-lei Kong, Zhi-mao Lu, Hao-liang Qi, Zhong-yuan Han. A machine learning approach to query generation in plagiarism source retrieval[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(10): 1556-1572.

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Abstract: 
Plagiarism source retrieval is the core task of plagiarism detection. It has become the standard for plagiarism detection to use the queries extracted from suspicious documents to retrieve the plagiarism sources. Generating queries from a suspicious document is one of the most important steps in plagiarism source retrieval. Heuristic-based query generation methods are widely used in the current research. Each heuristic-based method has its own advantages, and no one statistically outperforms the others on all suspicious document segments when generating queries for source retrieval. Further improvements on heuristic methods for source retrieval rely mainly on the experience of experts. This leads to difficulties in putting forward new heuristic methods that can overcome the shortcomings of the existing ones. This paper paves the way for a new statistical machine learning approach to select the best queries from the candidates. The statistical machine learning approach to query generation for source retrieval is formulated as a ranking framework. Specifically, it aims to achieve the optimal source retrieval performance for each suspicious document segment. The proposed method exploits learning to rank to generate queries from the candidates. To our knowledge, our work is the first research to apply machine learning methods to resolve the problem of query generation for source retrieval. To solve the essential problem of an absence of training data for learning to rank, the building of training samples for source retrieval is also conducted. We rigorously evaluate various aspects of the proposed method on the publicly available PAN source retrieval corpus. With respect to the established baselines, the experimental results show that applying our proposed query generation method based on machine learning yields statistically significant improvements over baselines in source retrieval effectiveness.

基于机器学习的抄袭源检索的查询生成方法

概要:抄袭源检索是抄袭检测的核心任务。使用从可疑文档提取的查询来检索抄袭源已成为抄袭源检索的标准方法。从可疑文档生成查询是源检索最重要的步骤。当前研究主要使用了基于启发式的查询生成方法。然而,每个启发式方法都有其优点,不同方法生成的查询可以获得不同的源检索结果,没有一种方法生成的查询的源检索性能可以在所有的文本片段上具有统计有效性地优于其他方法。这使得基于启发式的源检索查询生成方法的性能改善主要依赖专家经验。因此,很难开发一种可以克服现有启发式方法的新方法。本文提出使用统计机器学习方法解决源检索的查询生成问题,将源检索的查询生成形式化到一个排序学习的框架下,从备选查询中选择有利于提高源检索性能的查询,力争在每个可疑文档片段上获得最优的源检索性能。据我们所知,这是第一项应用机器学习方法解决源检索查询生成问题的工作。为了解决排序学习训练用例的缺失,提出了基于现有源检索语料构建查询生成语料的方法。在PAN抄袭源检索评测数据上的试验结果证明了该方法具有统计意义地优于多个基线方法。

关键词:抄袭检测;源检索;查询生成;机器学习;排序学习

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