Graph + Text Analysis

Plagiarism Detection Using Graph-Based Representation
http://www.mendeley.com/research/plagiarism-detection-using-graphbased-representation/

Plagiarism of material from the Internet is a widespread and growing problem. Several methods used to detect the plagiarism and similarity between the source document and suspected documents such as fingerprint based on character or n-gram. In this paper, we discussed a new method to detect the plagiarism based on graph representation; however, Preprocessing for each document is required such as breaking down the document into its constituent sentences. Segmentation of each sentence into separated terms and stop word removal. We build the graph by grouping each sentence terms in one node, the resulted nodes are connected to each other based on order of sentence within the document, all nodes in graph are also connected to top level node "Topic Signature". Topic signature node is formed by extracting the concepts of each sentence terms and grouping them in such node. The main advantage of the proposed method is the topic signature which is main entry for the graph is used as quick guide to the relevant nodes. which should be considered for the comparison between source documents and suspected one. We believe the proposed method can achieve a good performance in terms of effectiveness and efficiency.



テキスト分析における2部グラフクラスタリングの可能性(テキストの類似性・文処理モデル)
Possibilities of the Bipartite Graph Clustering in Text Analysis

http://ci.nii.ac.jp/naid/110004809727
テキスト解析にマルコフクラスタリング(MCL)、およびそれを独自に改良したリカレント・マルコフクラスタリング(RMCL)を利用する場合、有効なデータ取得法として、キーワードと共起語のペアに基づく、2部グラフ化の方法を提案し、MCL-RMCLによる2部グラフクラスタリングの計算結果を、従来のベクトル空間モデルに基づく多変量解析と比較、有効性を検証する.