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Auto-CORPus: a natural language processing tool for standardising and reusing biomedical literature

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Title: Auto-CORPus: a natural language processing tool for standardising and reusing biomedical literature
Authors: Beck, T
Shorter, T
Hu, S
Li, Z
Sun, S
Popovici, C
McQuibban, NAR
Makraduli, F
Yeung, C
Rowlands, T
Posma, JM
Item Type: Journal Article
Abstract: To analyse large corpora using machine learning and other Natural Language Processing (NLP) algorithms, the corpora need to be standardised. The BioC format is a community-driven simple data structure for sharing text and annotations, however there is limited access to biomedical literature in BioC format and a lack of bioinformatics tools to convert online publication HTML formats to BioC. We present Auto-CORPus (Automated pipeline for Consistent Outputs from Research Publications), a novel NLP tool for the standardisation and conversion of publication HTML and table image files to three convenient machine-interpretable outputs to support biomedical text analytics. Firstly, Auto-CORPus can be configured to convert HTML from various publication sources to BioC. To standardise the description of heterogenous publication sections, the Information Artifact Ontology is used to annotate each section within the BioC output. Secondly, Auto-CORPus transforms publication tables to a JSON format to store, exchange and annotate table data between text analytics systems. The BioC specification does not include a data structure for representing publication table data, so we present a JSON format for sharing table content and metadata. Inline tables within full-text HTML files and linked tables within separate HTML files are processed and converted to machine-interpretable table JSON format. Finally, Auto-CORPus extracts abbreviations declared within publication text and provides an abbreviations JSON output that relates an abbreviation with the full definition. This abbreviation collection supports text mining tasks such as named entity recognition by including abbreviations unique to individual publications that are not contained within standard bio-ontologies and dictionaries. The Auto-CORPus package is freely available with detailed instructions from GitHub at https://github.com/omicsNLP/Auto-CORPus.
Editors: Ruch, P
Issue Date: 15-Feb-2022
Date of Acceptance: 21-Jan-2022
URI: http://hdl.handle.net/10044/1/94647
DOI: 10.3389/fdgth.2022.788124
ISSN: 2673-253X
Publisher: Frontiers Media
Journal / Book Title: Frontiers in Digital Health
Volume: 4
Copyright Statement: © 2022 Beck, Shorter, Hu, Li, Sun, Popovici, McQuibban, Makraduli, Yeung, Rowlands and Posma. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Sponsor/Funder: Medical Research Council (MRC)
Medical Research Council
Funder's Grant Number: MR/S004033/1
Keywords: Natural Language Processing
text mining
health data
biomedical literature
Notes: Published in the Health Informatics section of the journal
Edition: Healthcare Text Analytics: Unlocking the Evidence from Free Text, Volume II
Publication Status: Published
Open Access location: https://www.biorxiv.org/content/10.1101/2021.01.08.425887v2
Article Number: ARTN 788124
Appears in Collections:Department of Metabolism, Digestion and Reproduction
Department of Surgery and Cancer