A Novel Two-Fold Loss Function for Data Clustering and Reconstruction: Application to Document Analysis

Abstract

In the midst of the ongoing COVID-19 pandemic, there has been a surge in scientific literature aimed at understanding the virus and its impact. However, it has become challenging for a researcher to deal with thousands of articles published daily. This paper proposes a novel deep-learning architecture to organize a large dataset of COVID-19-related scientific literature and provides a clear overview of the current state of knowledge. The proposed model is developed based on two main bases to ensure robustness and efficiency. In particular, we trained a denoising autoencoder with clean and noisy data to make the model can balance, preserving the underline structure and generalizing the new unseen data. Furthermore, the cornerstone of the proposed architecture lies in training the autoencoder using a two-fold objective function that jointly incorporates the data’s reconstruction and clustering. The advantage behind this combination is to avoid the distortion of the latent space and to improve the model efficiency. Afterward, we use the Latent Dirichlet Allocation (LDA) to analyze the document’s topics. For the sake of computational efficiency, instead of feeding the LDA with the whole dataset of documents, we fed it with the clusters produced in the phase of dimensionality reduction and clustering to count the frequency of topics in each cluster. The model was trained on a large public corpus of COVID-19-related articles and evaluated using a set of evaluation metrics. Experimental results indicate the superiority of our proposed model compared to several recent studies.

Keywords

• Clustering
• COVID-19
• deep learning
• dimensionality reduction
• document organization
• topic modeling