Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge (1611.10277v4)

Abstract: While generative models such as Latent Dirichlet Allocation (LDA) have proven fruitful in topic modeling, they often require detailed assumptions and careful specification of hyperparameters. Such model complexity issues only compound when trying to generalize generative models to incorporate human input. We introduce Correlation Explanation (CorEx), an alternative approach to topic modeling that does not assume an underlying generative model, and instead learns maximally informative topics through an information-theoretic framework. This framework naturally generalizes to hierarchical and semi-supervised extensions with no additional modeling assumptions. In particular, word-level domain knowledge can be flexibly incorporated within CorEx through anchor words, allowing topic separability and representation to be promoted with minimal human intervention. Across a variety of datasets, metrics, and experiments, we demonstrate that CorEx produces topics that are comparable in quality to those produced by unsupervised and semi-supervised variants of LDA.

• The paper introduces Correlation Explanation (CorEx), an information-theoretic topic modeling approach that bypasses generative assumptions and requires minimal domain knowledge.
• CorEx incorporates anchor words to flexibly integrate domain knowledge, enhancing its ability to represent less dominant or nuanced themes.
• Empirical evaluation shows CorEx performs comparably to LDA on various metrics while offering significant computational speedups due to efficient sparsity optimization.

Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge

The paper introduces Correlation Explanation (CorEx), an information-theoretic approach to topic modeling which diverges from the typical generative model assumptions of methods like Latent Dirichlet Allocation (LDA). CorEx seeks to uncover latent topics by maximizing their informativeness about documents, circumventing the need for predefined generative mechanisms. This paper presents an in-depth analysis and innovative enhancements to topic modeling through the CorEx framework, focusing particularly on the incorporation of domain knowledge via anchor words and leveraging the information bottleneck.

CorEx is framed within the field of information theory, eschewing traditional generative models for topic discovery. The model capitalizes on the concept of total correlation among word types, aiming to capture complex dependencies without presupposing document generation processes. Notably, this framework permits straightforward extensions into hierarchical and semi-supervised variants without necessitating additional modeling constraints. The resultant topics demonstrate quality comparable to those produced by unsupervised and semi-supervised LDA variants across diverse datasets and experiments.

Key Contributions and Findings

1. Efficient Sparsity Optimization: CorEx undergoes modifications to exploit sparsity in data, enhancing computational efficiency significantly. By restructuring the update process to focus on non-zero document-word entries, the CorEx framework accelerates processing times while maintaining strong hierarchical topic representations.
2. Anchoring Strategies: Through the introduction of anchor words, the paper devises methods to flexibly integrate domain knowledge into the CorEx model. This mechanism allows CorEx to promote representation of less dominant themes, making it particularly robust for complex topics or rare subject areas that may otherwise be overlooked.
3. Empirical Evaluation: Across various metrics, including topic coherence and document clustering, CorEx consistently performs on par with or surpasses traditional LDA-based models. The substantial computational speedup and coherent topic production position CorEx as a viable alternative in scenarios where large-scale document analysis is warranted.

Implications and Future Directions

The implications of this research are multifaceted, impacting both theoretical and practical aspects of topic modeling. The information-theoretic basis of CorEx allows researchers to reduce reliance on generative assumptions, making this approach adaptable across diverse datasets and contexts. Additionally, the anchoring mechanism provides a novel avenue to introduce domain expertise, enhancing the interpretability and relevance of topics generated in domains requiring nuanced understanding.

Future advancements in AI could leverage the lightweight and flexible structure of CorEx to explore other latent variable models where standard generative approaches may falter. Exploration into deeper hierarchical models and semi-supervised extensions could further strengthen the applicability of the CorEx framework in complex, multi-layered data environments.

In summary, the anchored Correlation Explanation offers a compelling alternative in topic modeling, especially for domains requiring minimal intervention to capture elusive or nuanced themes. By maximizing total correlation without stringent generative assumptions, CorEx provides a robust framework for researchers looking to derive meaningful document insights efficiently.