Abstract

Ever since we entered the digital communication era, the ease of information sharing through the Internet has encouraged online literature searching. With this comes the potential risk of a rise in academic misconduct and intellectual property theft. As concerns over plagiarism grow, more attention has been directed towards automatic plagiarism detection. This is a computational approach which assists humans in judging whether pieces of texts are plagiarised. However, most existing plagiarism detection approaches are limited to superficial, brute-force string- matching techniques. If the text has undergone substantial semantic and syntactic changes, string-matching approaches do not perform well. In order to identify such changes, linguistic techniques which are able to perform a deeper analysis of the text are needed. To date, very limited research has been conducted on the topic of utilising linguistic techniques in plagiarism detection.

This thesis provides novel perspectives on plagiarism detection and plagiarism direction identification tasks. The hypothesis is that original texts and rewritten texts exhibit significant but measurable differences, and that these differences can be captured through statistical and linguistic indicators. To investigate this hypothesis, four main research objectives are defined.

First, a novel framework for plagiarism detection is proposed. It involves the use of Natural Language Processing techniques, rather than only relying on the traditional string-matching approaches. The objective is to investigate and evaluate the influence of text pre-processing, and statistical, shallow and deep linguistic techniques using a corpus-based approach. This is achieved by evaluating the techniques in two main experimental settings.

Second, the role of machine learning in this novel framework is investigated. The objective is to determine whether the application of machine learning in the plagiarism detection task is helpful. This is achieved by comparing a threshold-setting approach against a supervised machine learning classifier.

Third, the prospect of applying the proposed framework in a large-scale scenario is explored. The objective is to investigate the scalability of the proposed framework and algorithms. This is achieved by experimenting with a large-scale corpus in three stages. The first two stages are based on longer text lengths and the final stage is based on segments of texts.

Finally, the plagiarism direction identification problem is explored as supervised machine learning classification and ranking tasks. Statistical and linguistic features are investigated individually or in various combinations. The objective is to introduce a new perspective on the traditional brute-force pair-wise comparison of texts. Instead of comparing original texts against rewritten texts, features are drawn based on traits of texts to build a pattern for original and rewritten texts. Thus, the classification or ranking task is to fit a piece of text into a pattern.

The framework is tested by empirical experiments, and the results from initial experiments show that deep linguistic analysis contributes to solving the problems we address in this thesis. Further experiments show that combining shallow and deep techniques helps improve the classification of plagiarised texts by reducing the number of false negatives. In addition, the experiment on plagiarism direction detection shows that rewritten texts can be identified by statistical and linguistic traits. The conclusions of this study offer ideas for further research directions and potential applications to tackle the challenges that lie ahead in detecting text reuse.

BibTeX

@PhdThesis{chong-phd,
   author =   {Miranda Chong},
   title =    {A Study on Plagiarism Detection and Plagiarism Direction 
               Identification Using Natural Language Processing Techniques},
   year =     {2013},
   address =  {Wolverhampton, UK},
   month =    {July},
   URL =      {http://clg.wlv.ac.uk/papers/chong-thesis.pdf}
}