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

ABSTRACT:

Motivation

Recent initiatives for federal grant transparency allow direct knowledge extraction from large volumes of grant texts, serving as a powerful alternative to traditional surveys. However, its computational modeling is challenging as grants are usually multifaceted with constantly evolving topics.

Results

We propose Turtling, a time-aware neural topic model with three unique characteristics. First, Turtling employs pretrained biomedical word embedding to extract research topics. Second, it leverages a probabilistic time-series model to allow smooth and coherent topic evolution. Lastly, Turtling leverages additional topic diversity loss and funding institute classification loss to improve topic quality and facilitate funding institute prediction. We apply Turtling<

SUBMITTER: Zhang R 

PROVIDER: S-EPMC11216609 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature