Approaches for Source Retrieval and Text Alignment of Plagiarism Detection Notebook for PAN at CLEF 2013

In this paper, we describe our approach at the PAN@CLEF2013 plagiarism detection competition. In sub-task of Source Retrieval, a method combined TF-IDF, PatTree and Weighted TF-IDF to extract the keywords of suspicious documents as queries to retrieve the plagiarism source document is proposed. In sub-task of Text Alignment, a method based on sentence similarity is presented. Our text alignment algorism and similar sentences merging algorism, called Bilateral Alternating Merging Algorithm, are described in detail. The great development of Internet makes it easier for people to search, copy, save, and reuse online sources. Copying another author's text and claiming its authorship is called plagiarism [1]. During the last decade, automated plagiarism detection in natural languages have attracted considerable attention from research and industry, which takes the advantage of recent developments in related fields like information retrieval, cross-language information retrieval, natural language processing, machine learning, and artificial intelligence. PAN@CLEF is dedicated to providing an environment which consists of a large scale corpus of artificial plagiarism and detection quality measures to evaluate the algorithms of plagiarism detection. There are two sub-tasks in PAN@CLEF2013: source retrieval and text alignment. The remaining sections of this paper introduce the methods we have taken in this year's competition. The task of source retrieval is to retrieve all plagiarized sources while minimizing retrieval costs [2]. One document plagiarizes another document by simple cut–paste manipulations, minor or wholesale alternations and more ambiguity rewriting. One of the difficulties of efficiently detecting plagiarism source is to search the source in