CliMedBERT: A Pre-trained Language Model for Climate and Health-related Text

TL;DR

This paper introduces CliMedBERT, a domain-specific language model designed to improve understanding and analysis of climate and health-related texts, aiding policy development and scientific knowledge synthesis.

Contribution

It is the first to develop multiple domain-specific language models for climate and health, facilitating tasks like fact-checking and relation extraction.

Findings

Proposed the development of CliMedBERT models.

Plans to release models, resources, and code for research use.

Addresses the challenge of synthesizing multidisciplinary climate-health science.

Abstract

Climate change is threatening human health in unprecedented orders and many ways. These threats are expected to grow unless effective and evidence-based policies are developed and acted upon to minimize or eliminate them. Attaining such a task requires the highest degree of the flow of knowledge from science into policy. The multidisciplinary, location-specific, and vastness of published science makes it challenging to keep track of novel work in this area, as well as making the traditional knowledge synthesis methods inefficient in infusing science into policy. To this end, we consider developing multiple domain-specific language models (LMs) with different variations from Climate- and Health-related information, which can serve as a foundational step toward capturing available knowledge to enable solving different tasks, such as detecting similarities between climate- and health-related concepts, fact-checking, relation extraction, evidence of health effects to policy text generation, and more. To our knowledge, this is the first work that proposes developing multiple domain-specific language models for the considered domains. We will make the developed models, resources, and codebase available for the researchers.