NDLI: Term co-occurrence-based corpus network

Content Provider	IEEE Xplore Digital Library
Author	Jung-Hee Park Jeong-Hyun Kim Seong-Bae Park Sang-Jo Lee
Copyright Year	2004
Description	Author affiliation: Dept. of Comput. Eng., Kyungpook Nat. Univ., Daegu, South Korea (Jung-Hee Park)
Abstract	This paper draws the term nodes and their links in corpus network based on term co-occurrence, and resultantly grasps its properties, using the mathematical graphs and the network-related physical measures. The topological processing of singular value decomposition (SVD), which compresses a large amount of co-occurrence information into a much smaller space, is similar to the common feature of feed-forward neural network systems where a large number of inputs are connected to a fairly small number of hidden layer nodes. Until now, the diagonal values by SVD have not been interpreted in full and the statistical step of hidden nodes depends only on the input. It is supposed that this study will provide the visual clues, which were described in the corpus, of the contraction-expansion homeostasis of the corpus network, and will help for actualizing the ambiguous axis in a space generated by SVD and characterizing the more efficient input-dependant steps of hidden nodes.
Starting Page	2957
Ending Page	2960
File Size	1042802
Page Count	4
File Format	PDF
ISBN	0780387309
DOI	10.1109/IECON.2004.1432281
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-11-02
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feedforward systems Neural networks Feedforward neural networks Frequency Computer networks Physics computing Art Statistics Character generation Information retrieval
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	ndl-support@iitkgp.ac.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Semantics-Based Art Image Retrieval Using Linguistic Variable

Reducing computations in incremental learning for feedforward neural network with long-term memory

An information criterion for optimal neural network selection

Cluster computing for neural network based anomaly detection

Feedforward Neural Network Methodology

Improving character recognition rate by a multi-net neural classifier

A neural network model of hysteresis

Feature Bispectra Based Real Time FM Signal Recognition Using Adaptive Incremental Learning Feedforward Neural Network

Implementation of an oversize neural network on DAP-510

Term co-occurrence-based corpus network

Similar Documents

Semantics-Based Art Image Retrieval Using Linguistic Variable

Reducing computations in incremental learning for feedforward neural network with long-term memory

An information criterion for optimal neural network selection

Cluster computing for neural network based anomaly detection

Feedforward Neural Network Methodology

Improving character recognition rate by a multi-net neural classifier

A neural network model of hysteresis

Feature Bispectra Based Real Time FM Signal Recognition Using Adaptive Incremental Learning Feedforward Neural Network

Implementation of an oversize neural network on DAP-510

Term co-occurrence-based corpus network