Utilizing Hubel Wiesel models for semantic associations and topics extraction from unstructured text

Publication Type

Conference Proceeding Article

Publication Date

8-2011

Abstract

There is a desire to extract and make better use of unstructured textual information available on the web. Semantic cognition opens new avenues in the utilization of this information. In this research, we extended the Hubel Wiesel model of hierarchical visual representation to extract semantic information from text. The unstructured text was preprocessed to a suitable input for Hubel Wiesel model. The threshold at each layer for neuronal growth was chosen as a ramp function of the level. Probabilistic approach was used for all post processing steps like prediction, word association, labeling, gist extraction etc. Equivalence with the Topics model was used to arrive at conditional probabilities in our model. We validated our model on three datasets and the model generated reasonable semantic associations. We evaluated the model based on top level clustering, label generation and word association.

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 2011 International Joint Conference on Neural Networks, San Jose, California, 2011 July 31 - August 5

ISBN

9781424496372

Identifier

10.1109/IJCNN.2011.6033316

Publisher

IEEE

City or Country

San Jose

Additional URL

http://doi.org/10.1109/IJCNN.2011.6033316

This document is currently not available here.

Share

COinS