Source Retrieval Based on Learning to Rank and Text Alignment Based on Plagiarism Type Recognition for Plagiarism Detection

This paper regards the query keywords selection problem in source retrieval as learning a ranking model to choose the method of keywords extraction over suspicious document segments. Four basic methods are used in our ranking function: BM25, TFIDF, TF and EW. Then, a ranking model based on Ranking SVM is proposed to rank the query keywords group which is contributed to get the higher evaluation measure F. In our ranking model, achieving the best performance measure F of source retrieval is used as the target of learning to rank. In text alignment, a novel method based on the plagiarism type recognition model is proposed. This approach employs the distinct strategies to detect the plagiarism text according the different plagiarism type. The plagiarism type recognition model is based on logical regression model. The experimental results on PAN 2014 plagiarism detection corpus indicate the efficiency of the proposed methods.