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Related Experiment Videos

Multilayer SOM with tree-structured data for efficient document retrieval and plagiarism detection.

Tommy W S Chow¹, M K M Rahman

¹Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong. eetchow@cityu.edu.hk

IEEE Transactions on Neural Networks

|August 1, 2009

Summary

This summary is machine-generated.

This study introduces a novel system for document retrieval and plagiarism detection using a multilayer self-organizing map (MLSOM). The approach accurately compares full documents, enhancing context understanding beyond traditional keyword methods.

Related Experiment Videos

Area of Science:

Computer Science
Information Retrieval
Artificial Intelligence

Background:

Existing document retrieval (DR) and plagiarism detection (PD) systems often rely on limited keyword or line-based comparisons.
Traditional methods struggle to capture the full contextual information inherent in document structures.
There is a need for computationally effective and accurate methods for comprehensive document analysis.

Purpose of the Study:

To propose a new document retrieval and plagiarism detection system utilizing a multilayer self-organizing map (MLSOM).
To develop a method that compares full documents, rather than just keywords or lines, for enhanced accuracy.
To leverage a tree-structured document representation for capturing hierarchical contextual features.

Main Methods:

Documents are modeled using a rich, tree-structured representation, including features at document, page, and paragraph levels.
A multilayer self-organizing map (MLSOM) algorithm is employed as an effective clustering and comparison mechanism.
Novel local matching techniques and two MLSOM-based plagiarism detection methods are developed for text document comparison.

Main Results:

The tree-structured data representation proves effective for both document retrieval and plagiarism detection.
The MLSOM algorithm provides a computationally efficient solution for handling complex, tree-structured data.
Experimental results demonstrate the proposed approach's computational efficiency and high accuracy in DR and PD tasks.

Conclusions:

The proposed MLSOM-based system offers a significant advancement in document retrieval and plagiarism detection.
The tree-structured document representation effectively captures contextual information, outperforming traditional methods.
The approach is validated as both computationally efficient and accurate for real-world document analysis applications.