Keep it simple with time: a reexamination of probabilistic topic detection models.

Area of Science:

• Natural Language Processing
• Information Retrieval
• Machine Learning

Background:

• Topic detection (TD) is crucial for managing large volumes of information, particularly in news streams.
• Existing methods face challenges in effectively distinguishing relevant content from noise.

Purpose of the Study:

• To introduce a simple and effective topic detection model, the temporal Discriminative Probabilistic Model (DPM).
• To evaluate DPM's performance against established probabilistic models on benchmark datasets.

Main Methods:

• Developed the temporal Discriminative Probabilistic Model (DPM).
• Compared DPM with mixture models (e.g., von-Mises Fisher) and mixed membership models (e.g., Latent Dirichlet Allocation).
• Utilized the TDT3 dataset for benchmarking.

Main Results:

• DPM demonstrated effectiveness for both offline and online topic detection.
• Complex models like LDA did not necessarily yield superior results, with LDA performing poorly under variational inference.
• The simplicity of DPM makes it a practical choice, avoiding issues associated with high parameter counts in models like LDA.

Conclusions:

• The temporal Discriminative Probabilistic Model (DPM) is a theoretically sound and practically effective approach to topic detection.
• A relatively simple, time-aware probabilistic model can outperform more complex alternatives for document-level topic detection.