Symbol-based entity marker highlighting for enhanced text mining in materials science with generative AI

TL;DR

This paper introduces a hybrid text-mining framework with symbolic entity markers that significantly improves scientific data extraction from unstructured materials science literature using generative AI.

Contribution

It presents a novel hybrid approach combining multi-step and direct methods with entity marker-based annotations to enhance entity recognition and data structuring in scientific texts.

Findings

Outperforms previous entity recognition methods on benchmark datasets

Achieves up to 58% improvement in entity-level F1 score

Achieves up to 83% improvement in relation-level F1 score

Abstract

The construction of experimental datasets is essential for expanding the scope of data-driven scientific discovery. Recent advances in natural language processing (NLP) have facilitated automatic extraction of structured data from unstructured scientific literature. While existing approaches-multi-step and direct methods-offer valuable capabilities, they also come with limitations when applied independently. Here, we propose a novel hybrid text-mining framework that integrates the advantages of both methods to convert unstructured scientific text into structured data. Our approach first transforms raw text into entity-recognized text, and subsequently into structured form. Furthermore, beyond the overall data structuring framework, we also enhance entity recognition performance by introducing an entity marker-a simple yet effective technique that uses symbolic annotations to highlight target entities. Specifically, our entity marker-based hybrid approach not only consistently outperforms previous entity recognition approaches across three benchmark datasets (MatScholar, SOFC, and SOFC slot NER) but also improve the quality of final structured data-yielding up to a 58% improvement in entity-level F1 score and up to 83% improvement in relation-level F1 score compared to direct approach.