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Turtling: a time-aware neural topic model on NIH grant data.

Ruiyi Zhang¹, Ziheng Duan¹, CheYu Lee¹

¹Department of Computer Science, University of California, Irvine, CA 92697, United States.

Bioinformatics Advances

|July 2, 2024

Summary

This summary is machine-generated.

Turtling, a novel time-aware neural topic model, enhances federal grant text analysis by tracking evolving research topics. This open-source tool improves topic quality and predicts funding institutes, offering valuable insights into scientific trends.

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Area of Science:

Computational linguistics
Bibliometrics
Health services research

Background:

Federal grant transparency initiatives necessitate advanced methods for knowledge extraction from large text volumes.
Traditional survey methods are insufficient for analyzing multifaceted and evolving grant topics.
Computational modeling of grant texts presents challenges due to their complexity and dynamic nature.

Purpose of the Study:

To introduce Turtling, a time-aware neural topic model designed for analyzing federal grant texts.
To improve the quality of extracted research topics and enable prediction of funding institutes.
To provide insights into the evolution of research topics over time.

Main Methods:

Utilizing pretrained biomedical word embeddings for research topic extraction.
Employing a probabilistic time-series model for smooth and coherent topic evolution.
Incorporating topic diversity loss and funding institute classification loss for enhanced topic quality and prediction.

Main Results:

Turtling significantly outperforms existing methods on topic quality metrics when applied to NIH grant texts.
The model successfully detects research topic trends across decades, offering insights into scientific evolution.
Demonstrated effectiveness in improving topic quality and facilitating funding institute prediction.

Conclusions:

Turtling is a valuable tool for analyzing federal grant texts, offering superior topic modeling capabilities.
The model provides a dynamic view of research trends, aiding in understanding scientific progress.
Turtling is available as open-source software, promoting wider adoption in grant text analysis.