Analyzing the impact of climate change on critical infrastructure from the scientific literature: A weakly supervised NLP approach

TL;DR

This paper presents a weakly supervised NLP method that rapidly labels large climate and infrastructure literature corpora, enabling efficient topic analysis and supporting policy decisions on climate change impacts.

Contribution

The authors develop a weak supervision approach that significantly reduces labeling time, facilitating targeted literature analysis in climate change and infrastructure research.

Findings

Labels a large corpus in about 13 hours using weak supervision.

Enables efficient identification of relevant documents and topics.

Supports rapid trend discovery at the intersection of climate hazards and infrastructure.

Abstract

Natural language processing (NLP) is a promising approach for analyzing large volumes of climate-change and infrastructure-related scientific literature. However, best-in-practice NLP techniques require large collections of relevant documents (corpus). Furthermore, NLP techniques using machine learning and deep learning techniques require labels grouping the articles based on user-defined criteria for a significant subset of a corpus in order to train the supervised model. Even labeling a few hundred documents with human subject-matter experts is a time-consuming process. To expedite this process, we developed a weak supervision-based NLP approach that leverages semantic similarity between categories and documents to (i) establish a topic-specific corpus by subsetting a large-scale open-access corpus and (ii) generate category labels for the topic-specific corpus. In comparison with a months-long process of subject-matter expert labeling, we assign category labels to the whole corpus using weak supervision and supervised learning in about 13 hours. The labeled climate and NCF corpus enable targeted, efficient identification of documents discussing a topic (or combination of topics) of interest and identification of various effects of climate change on critical infrastructure, improving the usability of scientific literature and ultimately supporting enhanced policy and decision making. To demonstrate this capability, we conduct topic modeling on pairs of climate hazards and NCFs to discover trending topics at the intersection of these categories. This method is useful for analysts and decision-makers to quickly grasp the relevant topics and most important documents linked to the topic.