Feed-Forward Network Model for Multi-Document Relation Classification


  • Naomie Salim Universiti Teknologi Malaysia




Using feed-forward artificial neural network to classify multi-document relation is the subject of this paper. Sentences across topically related documents can often be linked by means of relations that exist between them. In this study, we aim to identify four types of relations, namely, Identity, Subsumption, Description and Overlap. We propose to use neural network learning model for the task of classification; multi-class classification, in this case. The performance of our proposed approach was measured using Precision, Recall and F-measure. The experimental findings demonstrate that better results can be obtained by using the proposed approach when compared with the widely used SVM classifier

Author Biography

Naomie Salim, Universiti Teknologi Malaysia

Dr. Naomie Salim has contributed her service to Universiti Teknologi Malaysia for more than 20 years. Her contribution in the Databases and Information Retrieval field is illustrated by the numerous researches that she led and her highly cited publications in the field. She has also continuously serves as an administrator at the university, as a postgraduate coordinator, Head of Department and later, the Deputy Dean of Research and Postgraduate Studies at the Faculty of Computer Science and Information Systems, a capacity in which she is currently serving for the third term.



How to Cite

Salim, N. (2014). Feed-Forward Network Model for Multi-Document Relation Classification. International Journal of Innovative Computing, 3(1). https://doi.org/10.11113/ijic.v3n1.17