Neural network

An exploration in Document Retrieval System

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

As more and more information is stored electronically, the demand for intelligent methods becomes increasingly urgent. The basic belief underlying this research is that a truly helpful Document Retrieval System must `understand' what the user is looking for. A neural network has information processing structures attributes for adaptation to fulfill the needs within an information environment. These attributes are suitable to be used in a Document Retrieval System in order to build a faster, efficient and user-friendly system. This paper presents an experimental research on the effectiveness of a neural network model in document retrieval. The main purpose of this research is to demonstrate the feasibility of the proposed approach. The backpropagation neural network learning method is used to build up and employ an application domain knowledge for the Document Retrieval System. The knowledge is acquired from examples of queries and relevant documents. Then, another collection of queries is used to test the effectiveness of the system. The results are analyzed based on the aspects of document retrieval. This research showed that the Document Retrieval System can perform better and user friendly within feasible domain knowledge system with effective learning. But, learning implementation with big learning pattern can create more errors which can limit the process to generate the feasible domain knowledge system.

Original languageEnglish
Title of host publicationIEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume1
Publication statusPublished - 2000
Event2000 TENCON Proceedings - Kuala Lumpur, Malaysia
Duration: 24 Sep 200027 Sep 2000

Other

Other2000 TENCON Proceedings
CityKuala Lumpur, Malaysia
Period24/9/0027/9/00

Fingerprint

Information retrieval systems
Neural networks
Backpropagation

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Muniyandi, R. C. (2000). Neural network: An exploration in Document Retrieval System. In IEEE Region 10 Annual International Conference, Proceedings/TENCON (Vol. 1)

Neural network : An exploration in Document Retrieval System. / Muniyandi, Ravie Chandren.

IEEE Region 10 Annual International Conference, Proceedings/TENCON. Vol. 1 2000.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Muniyandi, RC 2000, Neural network: An exploration in Document Retrieval System. in IEEE Region 10 Annual International Conference, Proceedings/TENCON. vol. 1, 2000 TENCON Proceedings, Kuala Lumpur, Malaysia, 24/9/00.
Muniyandi RC. Neural network: An exploration in Document Retrieval System. In IEEE Region 10 Annual International Conference, Proceedings/TENCON. Vol. 1. 2000
Muniyandi, Ravie Chandren. / Neural network : An exploration in Document Retrieval System. IEEE Region 10 Annual International Conference, Proceedings/TENCON. Vol. 1 2000.
@inproceedings{c4223f1edeec44608293fcfdb3a11239,
title = "Neural network: An exploration in Document Retrieval System",
abstract = "As more and more information is stored electronically, the demand for intelligent methods becomes increasingly urgent. The basic belief underlying this research is that a truly helpful Document Retrieval System must `understand' what the user is looking for. A neural network has information processing structures attributes for adaptation to fulfill the needs within an information environment. These attributes are suitable to be used in a Document Retrieval System in order to build a faster, efficient and user-friendly system. This paper presents an experimental research on the effectiveness of a neural network model in document retrieval. The main purpose of this research is to demonstrate the feasibility of the proposed approach. The backpropagation neural network learning method is used to build up and employ an application domain knowledge for the Document Retrieval System. The knowledge is acquired from examples of queries and relevant documents. Then, another collection of queries is used to test the effectiveness of the system. The results are analyzed based on the aspects of document retrieval. This research showed that the Document Retrieval System can perform better and user friendly within feasible domain knowledge system with effective learning. But, learning implementation with big learning pattern can create more errors which can limit the process to generate the feasible domain knowledge system.",
author = "Muniyandi, {Ravie Chandren}",
year = "2000",
language = "English",
volume = "1",
booktitle = "IEEE Region 10 Annual International Conference, Proceedings/TENCON",

}

TY - GEN

T1 - Neural network

T2 - An exploration in Document Retrieval System

AU - Muniyandi, Ravie Chandren

PY - 2000

Y1 - 2000

N2 - As more and more information is stored electronically, the demand for intelligent methods becomes increasingly urgent. The basic belief underlying this research is that a truly helpful Document Retrieval System must `understand' what the user is looking for. A neural network has information processing structures attributes for adaptation to fulfill the needs within an information environment. These attributes are suitable to be used in a Document Retrieval System in order to build a faster, efficient and user-friendly system. This paper presents an experimental research on the effectiveness of a neural network model in document retrieval. The main purpose of this research is to demonstrate the feasibility of the proposed approach. The backpropagation neural network learning method is used to build up and employ an application domain knowledge for the Document Retrieval System. The knowledge is acquired from examples of queries and relevant documents. Then, another collection of queries is used to test the effectiveness of the system. The results are analyzed based on the aspects of document retrieval. This research showed that the Document Retrieval System can perform better and user friendly within feasible domain knowledge system with effective learning. But, learning implementation with big learning pattern can create more errors which can limit the process to generate the feasible domain knowledge system.

AB - As more and more information is stored electronically, the demand for intelligent methods becomes increasingly urgent. The basic belief underlying this research is that a truly helpful Document Retrieval System must `understand' what the user is looking for. A neural network has information processing structures attributes for adaptation to fulfill the needs within an information environment. These attributes are suitable to be used in a Document Retrieval System in order to build a faster, efficient and user-friendly system. This paper presents an experimental research on the effectiveness of a neural network model in document retrieval. The main purpose of this research is to demonstrate the feasibility of the proposed approach. The backpropagation neural network learning method is used to build up and employ an application domain knowledge for the Document Retrieval System. The knowledge is acquired from examples of queries and relevant documents. Then, another collection of queries is used to test the effectiveness of the system. The results are analyzed based on the aspects of document retrieval. This research showed that the Document Retrieval System can perform better and user friendly within feasible domain knowledge system with effective learning. But, learning implementation with big learning pattern can create more errors which can limit the process to generate the feasible domain knowledge system.

UR - http://www.scopus.com/inward/record.url?scp=0034428909&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0034428909&partnerID=8YFLogxK

M3 - Conference contribution

VL - 1

BT - IEEE Region 10 Annual International Conference, Proceedings/TENCON

ER -