id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
iassist-1023	De, Suparna; Moss, Harry; Johnson, Jon; Li, Jenny; Pereira, Haeron; Jabbari, Sanaz 	Engineering a machine learning pipeline for automating metadata extraction from longitudinal survey questionnaires	2022	12	.pdf	application/pdf	4414	222	46	This necessitates a continuous build and integrate approach, with the different combinations of input data, feature engineering methods, model parameters and their resultant outputs being attached to an ML pipeline. Thus, this paper also showcases the integration of the abstraction of model parameters through pipelines (through Data Version Control (DVC) (Kuprieiev et al., 2021)) and automating the process of attaching metadata related to each model experiment (through ProvLake).	cache/iassist-1023.pdf	txt/iassist-1023.txt