GHTM: A Graph based Hybrid Topic Modeling Approach in Low-Resource Bengali Language

Abstract

Topic modeling is a Natural Language Processing (NLP) technique that is used to identify latent themes and extract topics from text corpora by grouping similar documents based on their most significant keywords. Although widely researched in English, topic modeling remains understudied in Bengali due to its morphological complexity, lack of adequate resources and initiatives. In this contribution, a novel Graph Convolutional Network (GCN) based model called GHTM (Graph-Based Hybrid Topic Model) is proposed. This model represents input vectors of documents as nodes in the graph, which GCN uses to produce semantically rich embeddings. The embeddings are then decomposed using Non-negative Matrix Factorization (NMF) to get the topical representations of the underlying themes of the text corpus. This study compares the proposed model against a wide range of Bengali topic modeling techniques, from traditional methods such as LDA, LSA, and NMF to contemporary frameworks such as BERTopic and Top2Vec on three Bengali datasets. The experimental results demonstrate the effectiveness of the proposed model by outperforming other models in topic coherence and diversity. In addition, we introduce a novel Bengali dataset called "NCTBText" sourced from Bengali textbook materials to enrich and diversify the predominantly newspaper-centric Bengali corpora.