Timeline: A Dynamic Hierarchical Dirichlet Process Model for Recovering Birth/Death and Evolution of Topics in Text Stream (1203.3463v1)

Abstract: Topic models have proven to be a useful tool for discovering latent structures in document collections. However, most document collections often come as temporal streams and thus several aspects of the latent structure such as the number of topics, the topics' distribution and popularity are time-evolving. Several models exist that model the evolution of some but not all of the above aspects. In this paper we introduce infinite dynamic topic models, iDTM, that can accommodate the evolution of all the aforementioned aspects. Our model assumes that documents are organized into epochs, where the documents within each epoch are exchangeable but the order between the documents is maintained across epochs. iDTM allows for unbounded number of topics: topics can die or be born at any epoch, and the representation of each topic can evolve according to a Markovian dynamics. We use iDTM to analyze the birth and evolution of topics in the NIPS community and evaluated the efficacy of our model on both simulated and real datasets with favorable outcome.

• The paper presents iDTM, a model that dynamically recovers topic birth, death, and evolution in time-sensitive document collections.
• It employs a recurrent Chinese restaurant franchise to integrate temporal dynamics with multi-topic modeling across epochs.
• Empirical tests on NIPS proceedings show superior predictive performance and adaptability compared to traditional dynamic topic models.

Dynamic Hierarchical Dirichlet Process for Topic Evolution in Text Streams

This essay examines the paper "Timeline: A Dynamic Hierarchical Dirichlet Process Model for Recovering Birth/Death and Evolution of Topics in Text Stream" by Amr Ahmed and Eric P. Xing. The paper introduces the Infinite Dynamic Topic Model (iDTM), which addresses the challenges of modeling the evolution of topics in time-sensitive document collections. The proposed model improves upon traditional topic modeling techniques by incorporating a temporal dimension that allows for the birth and death of topics, accommodating variations in topic trends and word distributions.

The iDTM assumes documents are organized into epochs, maintaining the order between epochs while considering the exchangeability of documents within the same epoch. This approach permits an unbounded number of topics, which can evolve through Markovian dynamics, reflecting changes in the text stream over time. Unlike previous models, iDTM supports the dynamic adaptation of the number of topics, allowing them to emerge or expire at any epoch. This is facilitated by a first-order state space model that dictates the evolution of topics' word distributions and a rich-gets-richer dynamic governing topic popularity.

The iDTM is constructed on a recurrent Chinese restaurant franchise (RCRF) process, an extension of the Chinese restaurant process (CRP) that introduces temporal dependencies. This extension effectively allows for the modeling of multi-topic documents, overcoming limitations of previous single-topic temporal models. The RCRF combines temporal Dirichlet processes (TDPM) and hierarchical Dirichlet processes (HDP), creating a robust framework that integrates temporal evolution with topic admixture.

Empirical evaluation is conducted on both simulated and real datasets, specifically analyzing proceedings from the NIPS conference. Results exhibit iDTM's capability to accurately capture the lifespan and trends of topics over time. Noteworthy findings include iDTM's ability to adjust the number of topics in response to changes in the symmetrized KL-divergence of word distributions across epochs, demonstrating sensitivity to significant shifts in thematic content.

Quantitative analysis is presented in terms of held-out log likelihood comparisons with existing models, such as Dynamic Topic Models (DTM) and HDP. iDTM significantly outperforms these models, reflecting superior predictive power and adaptability. Sensitivity studies further elucidate the impact of hyperparameters on model performance, highlighting optimal settings for key parameters such as the trait variance in the random walk kernel and the strength of temporal dependencies.

In conclusion, the iDTM offers a comprehensive framework for analyzing evolving topics within textual data streams, capturing the nuanced dynamics of topic birth, death, and transformation. Future directions suggested include incorporating an extended Gibbs sampling methodology for hyperparameter optimization and adapting the model to accommodate diverse hierarchical structures, such as different academic conferences. By refining the scope and adaptability of topic models like iDTM, we can better understand thematic evolution across diverse disciplines and datasets.