Plagiarism source retrieval sorting model construction method and plagiarism source retrieval sorting method

Abstract

The invention provides a plagiarism source retrieval sorting model construction method and a plagiarism source retrieval sorting method. According to the plagiarism source retrieval sorting model construction method, training samples are utilized to train a predetermined sorting logic regression model through an order pair-based sorting learning manner on the basis of a degree of aggregation between each plagiarism source document of a reference document and the reference document until a value of a predetermined loss function is minimum, the predetermined loss function includes first and second sub-loss functions, the first sub-loss function represents a loss caused by sorting errors of order pairs formed on the basis of the plagiarism source documents and non-plagiarism source documentsof the reference document, and the second sub-loss function represents a loss caused by sorting errors of order pairs formed by plagiarism source documents with different degrees of aggregation. The plagiarism source retrieval sorting method utilizes the above obtained sorting model to resort retrieval results of suspicious documents. The above technology of the invention can more accurately sortthe source retrieval results of the suspicious documents in plagiarism detection.

Description

technical field [0001] The invention relates to information retrieval technology, in particular to a plagiarism source retrieval and sorting model building method and a plagiarism source retrieval and sorting method. Background technique [0002] In the general process of plagiarism detection source retrieval, plagiarism source retrieval algorithms usually filter the retrieval results to obtain plagiarism source documents that are finally text-aligned with suspicious documents. Among them, the performance of filtering is crucial to the performance of source retrieval and is an indispensable key step of source retrieval. [0003] At present, existing source retrieval and filtering techniques mainly adopt heuristic methods. However, it is difficult for the heuristic method to incorporate more effective features, and its performance improvement depends on the experience of experts and the discovery of effective filtering features. [0004] Compared with heuristic methods, mac...

AI Technical Summary

Problems solved by technology

However, currently only Williams et al. use classification-based machine learning methods (Williams K, Chen H H, Giles C L. Classifying and Tanking Search Engine Results as Potential Sources of Plagiarism [C]. Proceedings of the 2014ACM Symposium on Document Engineering. Fort Collins, CO, USA, September 16-19, 2014. ACM, 2014: 97-106.) to solve the filtering problem of source retrieval

[0005] However, the above existing technologies all have the problem of inaccurate source retrieval and filtering results.

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