Domain Classification of Biomedical Research Articles based on BiLSTM for Recommendation System

Abstract

Nowadays, Researchers and Scientists have plenty of resources for Research papers research repository available on the web. When the information amount increased day by day the researchers faced a new problem that they confused for selecting most relevant research paper actually they want to access. PubMed, for example, has an enormous collection of medical related Research papers. The Recommendation system helps researchers to retrieve the relevant paper and keeping the track of their research field. To improve the better response and achieving an accurate Recommendation system this proposed Domain Classification system is to classify similar domain of articles. This study focuses to extract the relevant domain from PubMed database by proposes a multilayered Recurrent Neural Network (RNN) model based on Bidirectional Long Short-Term Memory (BiLSTM). Experimental result shows RNN based BiLSTM on PubMed domain classification outperforms the traditional classifications for RS.