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Multilayer SOM With Tree-Structured Data for Efficient Document Retrieval and Plagiarism Detection

This paper presents a novel approach using Multilayer SOM for efficient document retrieval and plagiarism detection by incorporating tree-structured data. The method enhances retrieval accuracy by combining global and local characteristics, showing promising results. MLSOM serves as a practical computational solution, offering simplicity and effectiveness. However, the rate of failed plagiarism detection remains a drawback. Overall, this innovative application demonstrates the potential of MLSOM in text analysis.

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Multilayer SOM With Tree-Structured Data for Efficient Document Retrieval and Plagiarism Detection

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  1. Multilayer SOM With Tree-Structured Data for Efficient Document Retrieval and Plagiarism Detection Presenter : Cheng-Feng Weng Authors :Tommy W. S. Chow, M. K. M. Rahman 2009/10/12 TNN.18 (2009)

  2. Outline • Motivation • Objective • Method • Experiments • Conclusion • Comments

  3. Motivation Science ……. Computer ……. School …….. School of Computer Science …….. • Document Retrieval: • Term-Frequency Problem • Two doc. Containing similar term frequencies may be of different contextually when it spatial distribution of terms is very different. • Plagiarism Detective: • Paraphrasing Problem SOM …project…….. SOM …be mapped into……..

  4. Objective • It proposed a tree-structured document model with MLSOM for DR and PD. Global View Document ……. Tree-Structured Model DR Local View PD MLSOM

  5. Structured Representation of DF • A document is partitioned into pages that are further partitioned into paragraphs. Page 我是網頁 我是網頁 第一行 第二行 無言的第三行 我是網頁 第一行 第一行 第二行 <HTML> <HEAD> </HEAD> <BODY> 我是網頁<br> <p>第一行</p> <p>第二行</p> 無言的第三行 </BODY> </HTML> 無言的第三行 Paragraph 我是網頁

  6. Structured Representation of DF(cont.)

  7. Multilayer SOM • MLSOM was developed for handling tree-structured data.

  8. Multilayer SOM (cont.) • Similarity:

  9. Related Docs. MLSOM Retrieval Document Extract to tree-structure and project with PCA matrix Trained MLSOM

  10. Plagiarism Detective • Plagiarism Detective using Local Association (PDLA) Layer 3 SOM Related Docs. D1, D2, … D3, D4, …. D2, D6, … …

  11. Experiments • Document Retrieval:

  12. Experiments (cont.) • Plagiarism Detective:

  13. Conclusions • A new approach of DR and PD using tree-structured document representation and MLSOM is proposed. • It has shown that tree-structured representation enhances the retrieval accuracy by incorporating local characteristics with traditional global characteristics. • Computational Issue: • The MLSOM serves as an efficient computational solution for practical implementation.

  14. Comments • Advantage • Practical, Simple but efficient and effective • Drawback • Rate of fail plagiarism detective is still high • Application • …

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