Incorporating Domain Knowledge into Topic Modeling via Dirichlet Forest Priors

Area of Science:

• Computational Linguistics
• Machine Learning
• Statistical Modeling

Background:

• Topic modeling methods like Latent Dirichlet Allocation (LDA) are widely used for uncovering latent themes in text data.
• Incorporating prior domain knowledge into topic models can significantly enhance their relevance and interpretability.
• Existing methods often lack flexibility in integrating nuanced user-specified constraints on word-topic probabilities.

Purpose of the Study:

• To develop a novel topic modeling framework that effectively integrates user-provided domain knowledge.
• To introduce a Dirichlet Forest prior as a flexible mechanism for encoding constraints on word-topic distributions within LDA.
• To demonstrate the model's capability to adhere to and generalize from user-specified domain knowledge.

Main Methods:

• The proposed method extends the Latent Dirichlet Allocation (LDA) framework.
• A novel Dirichlet Forest prior, constructed as a mixture of Dirichlet tree distributions, is employed.
• Inference is performed using a collapsed Gibbs sampling algorithm.

Main Results:

• The Dirichlet Forest prior successfully incorporates user-specified domain knowledge regarding word-topic probabilities.
• Experiments on both synthetic and real-world datasets show the model's ability to follow user constraints.
• The model demonstrates generalization capabilities, performing well beyond the explicitly provided domain knowledge.

Conclusions:

• The proposed Dirichlet Forest prior offers a powerful and flexible approach to incorporating domain knowledge into topic modeling.
• This method enhances the interpretability and accuracy of topic models by leveraging expert insights.
• The framework shows promise for applications requiring domain-specific text analysis and information retrieval.