An artificial neural network that recognizes an ordered set of words in text mining task

Author

Bavan, A.S.

Author_Institution

Sch. of Eng. & Inf. Sci., Middlesex Univ., London, UK

fYear

2009

Firstpage

1

Lastpage

5

Abstract

This paper presents an artificial neural network based tool that locates an ordered set of words in a text. The network model is essentially a single layer network similar to Hopfield model that uses a Hebbian approach to activate the feature layer nodes (see section 2). This model was initially developed to go with our Connectionist Associative Memory Model (CAMM) and later found to be useful in text mining tasks as well. Further additions of features are currently under way to generate statistics and structural graphs based on semantic relationships.

Keywords

Hebbian learning; Hopfield neural nets; data mining; statistical analysis; text analysis; Hebbian approach; Hopfield model; artificial neural network; connectionist associative memory model; statistics; structural graphs; text mining task; words recognition; Artificial neural networks; Carbon capture and storage; Computer languages; Concurrent computing; Distributed computing; Educational institutions; Formal specifications; Lips; Text mining; Text recognition; Hebbian Approach; Hopfield Model; Neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Current Trends in Information Technology (CTIT), 2009 International Conference on the

Conference_Location

Dubai

Print_ISBN

978-1-4244-5754-0

Electronic_ISBN

978-1-4244-5756-4

Type

conf

DOI

10.1109/CTIT.2009.5423122

Filename

5423122