ESMARConf2022 Special Session 1: Review processes from A to Z (part 1) - Bronwen Hunter
Channel: ESMARConf
Date: 2022-02-22

Presenter: Bronwen Hunter
Session: Special Session 1: Review processes from A to Z (part 1)
Title: Using state-of-the-art transformer models to automate text classification in R
Abstract: The utilisation of automated classification tools from the field of Natural Language Processing (NLP) can massively decrease the amount of time required for the article screening stage of evidence synthesis. To achieve high accuracy, models often require huge volumes of ‘gold-standard’ labelled training data, which can be expensive and time-consuming to produce. As a result, ‘transfer learning’, in which NLP models, pre-trained on large corpora, are downloaded and finetuned on a smaller number of hand-labelled texts, is an increasingly popular method for achieving high-performance text classification. The availability of state-of-the-art transformer models via the open source ‘hugging face’ library has also improved the accessibility of this approach. However, materials outlining how to make use of such resources in R are limited. At ESMARCONF 2022, I will introduce and demonstrate how transfer learning can be carried out in R and seamlessly integrated with data collection from academic databases and internet sources.

Please cite as: Hunter, Bronwen (2022) Using state-of-the-art transformer models to automate text classification in R. Zenodo. ESMARConf2022 conference presentation. DOI: https://doi.org/10.5281/zenodo.6043287.