Application of machine reading comprehension techniques for named entity recognition in materials science.

ABSTRACT: Materials science is an interdisciplinary field that studies the properties, structures, and behaviors of different materials. A large amount of scientific literature contains rich knowledge in the field of materials science, but manually analyzing these papers to find material-related data is a daunting task. In information processing, named entity recognition (NER) plays a crucial role as it can automatically extract entities in the field of materials science, which have significant value in tasks such as building knowledge graphs. The typically used sequence labeling methods for traditional named entity recognition in material science (MatNER) tasks often fail to fully utilize the semantic information in the dataset and cannot effectively extract nested entities. Herein, we proposed to

SUBMITTER: Huang Z 

PROVIDER: S-EPMC11220966 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature