Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression (1206.3278v1)

Abstract: Although fully generative models have been successfully used to model the contents of text documents, they are often awkward to apply to combinations of text data and document metadata. In this paper we propose a Dirichlet-multinomial regression (DMR) topic model that includes a log-linear prior on document-topic distributions that is a function of observed features of the document, such as author, publication venue, references, and dates. We show that by selecting appropriate features, DMR topic models can meet or exceed the performance of several previously published topic models designed for specific data.

• The paper introduces DMR, a method that conditions topic distributions on arbitrary metadata to improve topic quality.
• It employs a log-linear feature mapping with stochastic EM and L-BFGS optimization, making inference both efficient and scalable.
• DMR outperforms traditional models like LDA in perplexity and likelihood, demonstrating its effectiveness in real-world text analysis.

Overview of "Topic Models Conditioned on Arbitrary Features with Dirichlet-Multinomial Regression"

The paper "Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression" by David Mimno and Andrew McCallum introduces an innovative approach to topic modeling that leverages arbitrary document features without the complex model modifications typically needed. This method, known as Dirichlet-multinomial regression (DMR), presents a conditional modeling framework that logically incorporates document metadata, such as authorship, publication venues, and dates, to enhance topic discovery and representation.

Key Contributions and Methodology

1. Conditional Framework: DMR stands apart from traditional generative models like Latent Dirichlet Allocation (LDA) by integrating metadata directly into topic distribution priors via a log-linear process. This conditioning enables the model to utilize observed document features effectively, offering a seamless method of applying metadata without requiring complex generative assumptions or extra sampling variables.
2. Technical Implementation: The DMR model maps document features to topic distributions using a feature matrix, enabling straightforward encoding of various metadata. The model performs optimization through a stochastic Expectation Maximization (EM) scheme coupled with the L-BFGS method, maintaining tractability in inference through Gibbs sampling.
3. Comparison with Existing Models: The authors demonstrate that DMR models can replicate and surpass numerous established metadata-focused topic models, such as the Author-Topic (AT) model and Topics Over Time (TOT) model, without the intricacies of extensive model redesign.
4. Performance Evaluation: Extensive experiments on a corpus derived from the Rexa database show that DMR exhibits excellent perplexity scores and empirical likelihood. It efficiently handles combinations of features that previously required separate model adjustments, facilitating rapid deployment across diverse applications.

Results and Implications

The results presented indicate that DMR can produce a superior fit and predictive capability compared to traditional models like LDA when metadata is present. Moreover, the performance difference is most notable in the domain of author and citation features, where DMR shows significant improvements in perplexity measures. Additionally, DMR demonstrates flexibility in handling complex feature sets without the training difficulties characteristic of sLDA or exponential family harmoniums.

From a practical standpoint, DMR's ability to integrate multiple and potentially complex metadata configurations offers substantial scalability for real-world text mining applications. Its adaptability allows researchers to craft topic models that are well-aligned with their datasets' unique properties, delivering customized insights without necessitating deep statistical expertise in model formulation.

Speculation on Future Developments

The extension of DMR to incorporate hybrid models that draw upon the strengths of both generative and conditional paradigms, such as combining elements of sLDA and DMR, presents a promising avenue for future investigation. These developments could further enhance the model's versatility in managing both observable features and latent structure in complex data environments.

Moreover, as the demand for nuanced models tailored to multifaceted datasets grows, DMR may become an essential tool for researchers promoting high-level, scalable text analysis across domains such as social media analytics, scientific literature mining, and beyond.

In summary, the proposed Dirichlet-multinomial regression model effectively leverages arbitrary metadata for enhanced topic modeling. Its simple yet powerful framework holds the potential to transform how researchers approach document analysis in metadata-rich contexts, offering both robust performance and operational simplicity.