EXSCLAIM!: Harnessing materials science literature for self-labeled microscopy datasets

EXSCLAIM!: Harnessing materials science literature for self-labeled microscopy datasets

Summary: This work introduces the EXSCLAIM! toolkit for the automatic extraction, separation, and caption-based natural language annotation of images from scientific literature. EXSCLAIM! is used to show how rule-based natural language processing and image recognition can be leveraged to construct a...

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Bibliographic Details
Main Authors:	Eric Schwenker, Weixin Jiang, Trevor Spreadbury, Nicola Ferrier, Oliver Cossairt, Maria K.Y. Chan
Format:	Article
Language:	English
Published:	Elsevier 2023-11-01
Series:	Patterns
Subjects:	DSML 2: Proof-of-concept: Data science output has been formulated, implemented, and tested for one domain/problem
Online Access:	http://www.sciencedirect.com/science/article/pii/S2666389923002222

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