To analyse large corpora using machine learning and other Natural Language Processing (NLP) algorithms, the corpora need to be standardized. 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 standardization 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 standardize 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.

MotivationThe availability of improved natural language processing (NLP) algorithms and models enable researchers to analyse larger corpora using open source tools. Text mining of biomedical literature is one area for which NLP has been used in recent years with large untapped potential. However, in order to generate corpora that can be analyzed using machine learning NLP algorithms, these need to be standardized. Summarizing data from literature to be stored into databases typically requires manual curation, especially for extracting data from result tables.ResultsWe present here an automated pipeline that cleans HTML files from biomedical literature. The output is a single JSON file that contains the text for each section, table data in machine-readable format and lists of phenotypes and abbreviations found in the article. We analyzed a total of 2,441 Open Access articles from PubMed Central, from both Genome-Wide and Metabolome-Wide Association Studies, and developed a model to standardize the section headers based on the Information Artifact Ontology. Extraction of table data was developed on PubMed articles and fine-tuned using the equivalent publisher versions.AvailabilityThe Auto-CORPus package is freely available with detailed instructions from Github at https://github.com/jmp111/AutoCORPus/.