Toward deep multi-view document clustering using enhanced semantic embedding and consistent context semantics

Multi-view document clustering (MVDC) is a sophisticated approach in natural language processing that leverages multiple representations or views of data to improve clustering performance. Existing solutions are challenging due to inconsistency of document views, high dimensions, and sparseness in text documents. On the other hand, existing MVDC-based methods often depend on the performance of bag-of-words and pretrained language models. However, these models usually do not consider contextual semantics and are suitable for single-view document clustering. This paper addresses these challenges by proposing a deep MVDC model that utilizes enhanced semantic embedding and consistent context semantics (SECS). SECS uses semantic embedding to address high-dimensional challenges by considering complementary semantic information. Meanwhile, SECS takes advantage of the potential benefits of view-consistent context semantics based on pretrained language models. The proposed model captures intricate semantic relationships between words and documents through advanced embedding techniques, ensuring a richer and more nuanced representation of textual content. Furthermore, by incorporating consistent context semantics, SECS maintains contextual integrity across multiple views, leading to more coherent and meaningful clusters. Experimental results on benchmark datasets demonstrate the superiority of our model over state-of-the-art MVDC methods, highlighting its effectiveness in improving clustering quality and interpretability.