Given the emergence of data science and machine learning throughout all aspects of
society, but particularly in the scientific domain, there is increased importance placed on …

Research publications are the major repository of scientific knowledge. However, their
unstructured and highly heterogenous format creates a significant obstacle to large-scale …

A large amount of materials science knowledge is generated and stored as text published in
peer-reviewed scientific literature. While recent developments in natural language …

A bottleneck in efficiently connecting new materials discoveries to established literature has
arisen due to an increase in publications. This problem may be addressed by using named …

Simple yet effective data augmentation techniques have been proposed for sentence-level
and sentence-pair natural language processing tasks. Inspired by these efforts, we design …

The automatic semantic structuring of scientific text allows for more efficient reading of
research articles and is an important indexing step for academic search engines. Sequential …

We present MatSci-NLP, a natural language benchmark for evaluating the performance of
natural language processing (NLP) models on materials science text. We construct the …

The discovery of new materials has a documented history of propelling human progress for
centuries and more. The behaviour of a material is a function of its composition, structure …

Deep learning is becoming a standard tool in chemistry and materials science. Although
there are learning materials available for deep learning, none cover the applications in …

The academic literature of social sciences records human civilization and studies human
social problems. With its large-scale growth, the ways to quickly find existing research on …