4 accepted papers
Scientific research articles, typically distributed in PDF format, contain valuable knowledge but remain challenging to convert into structured datasets due to fragmented workflows that separate parsing, annotation, and visualization. Existing annotation platforms operate on plain text, which requir
This paper presents a unified framework for extracting n-ary property information from materials science literature, addressing the critical challenge of capturing complex relationships that often span multiple sentences. We introduce three complementary approaches: RE-Composition, which transforms
The growing volume of scientific literature in polymer science presents a significant challenge for researchers attempting to extract and annotate domain-specific entities, such as polymer names, material properties, and related information. Manual annotation of these documents is both time-consumin…
Polymers are widely used in diverse fields, and the demand for efficient methods to extract and organize information about them is increasing. An automated approach that utilizes machine learning can accurately extract relevant information from scientific papers, providing a promising solution for a…