Integrating Document Clustering and Topic Modeling (1309.6874v1)

Abstract: Document clustering and topic modeling are two closely related tasks which can mutually benefit each other. Topic modeling can project documents into a topic space which facilitates effective document clustering. Cluster labels discovered by document clustering can be incorporated into topic models to extract local topics specific to each cluster and global topics shared by all clusters. In this paper, we propose a multi-grain clustering topic model (MGCTM) which integrates document clustering and topic modeling into a unified framework and jointly performs the two tasks to achieve the overall best performance. Our model tightly couples two components: a mixture component used for discovering latent groups in document collection and a topic model component used for mining multi-grain topics including local topics specific to each cluster and global topics shared across clusters.We employ variational inference to approximate the posterior of hidden variables and learn model parameters. Experiments on two datasets demonstrate the effectiveness of our model.

• The paper presents MGCTM, a unified framework that fuses document clustering with topic modeling to mutually enhance clustering accuracy and topic distinction.
• It employs variational inference for efficient parameter estimation, outperforming traditional methods like K-means and LDA on standard datasets.
• The model distinguishes between local and global topics, improving document summarization and offering clearer insights into complex text data.

Integrating Document Clustering and Topic Modeling: A Unified Framework

The paper "Integrating Document Clustering and Topic Modeling" by Pengtao Xie and Eric P. Xing addresses the intrinsic connection between document clustering and topic modeling. These tasks have conventionally been approached as separate processes; however, the authors propose their integration into a novel unified framework known as the Multi-Grain Clustering Topic Model (MGCTM). The paper thoroughly explores the ways in which document clustering and topic modeling can mutually enhance each other, resulting in improved performance in both tasks.

Key Contributions

The primary contribution of this paper is the development of MGCTM, a generative model that fuses document clustering and topic modeling into a single cohesive framework. This model leverages the strengths of each task to refine the outputs of the other. Document clustering is achieved through a mixture component that identifies latent groupings in a document collection. Concurrently, a topic model component extracts both local topics, specific to individual clusters, and global topics, shared across clusters. Such a design allows for the distillation of fine-grained topics that improve both cluster coherence and topic distinction.

A significant innovation in MGCTM is the capability to delineate between group-specific and corpus-wide topics, an ability not present in standard topic models like Latent Dirichlet Allocation (LDA). This structured topic differentiation enhances the interpretability and usefulness of the topics, facilitating more accurate document summarization and improved relevance in clustering.

Methodology

The authors propose using variational inference for approximating the posterior distributions of hidden variables and parameter estimation. Variational inference is known for its efficiency over traditional methods like Gibbs sampling, especially in handling large datasets as demonstrated in their experimental validation on the Reuters-21578 and 20-Newsgroups datasets.

The experimental setup evaluates the clustering accuracy and the coherence of inferred topics. The results show that MGCTM outperforms baseline models like K-means, normalized cut, and several matrix factorization approaches, such as Non-negative Matrix Factorization (NMF) and Latent Semantic Indexing (LSI). Notably, MGCTM surpasses other integrative approaches, including the Cluster-based Topic Model (CTM) and LDA-based naive clustering methods.

Results and Implications

The empirical results highlight two main observations: First, the integration of clustering and topic modeling significantly boosts performance over executing these tasks independently. The paper demonstrates improved clustering accuracy and a higher normalized mutual information score with MGCTM, showcasing its efficacy in real-world text mining applications.

Second, the differentiation between local and global topics achieved by MGCTM provides substantial interpretive power, offering clear insights into document collections. The capability to distinctly categorize topics allows for more precise document grouping and richer, more focused topic extraction.

Future Directions

While the paper is comprehensive in its exploration of integrating clustering and topic modeling, there remains potential for further refinements. The authors suggest exploring semi-supervised learning extensions, which could incorporate partial supervision through document pairwise constraints, enhancing clustering quality further with minimal supervision.

The implications for applied domains such as information retrieval, organizational data mining, and automated content management are profound. By fostering better coherence and relevance in both cluster and topic constructs, MGCTM serves as a robust toolset capable of addressing complex semantic structures in large-scale text datasets.

In conclusion, this paper presents a substantial advancement in addressing two foundational challenges in machine learning and natural language processing. By effectively merging the processes of document clustering and topic modeling, MGCTM sets a significant precedent for further integrated approaches in artificial intelligence.