{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["14(1)"],"submitter":["Lee B"],"pubmed_abstract":["Author affiliations are essential in bibliometric studies, requiring relevant information extraction from free-text affiliations. Precisely determining an author's location from their affiliation is crucial for understanding research networks, collaborations, and geographic distribution. Existing geoparsing tools using regular expressions have limitations due to unstructured and ambiguous affiliations, resulting in erroneous location identification, especially for unconventional variations or misspellings. Moreover, their inefficient handling of big datasets hampers large-scale bibliometric studies. Though machine learning-based geoparsers exist, they depend on explicit location information, creating challenges when detailed geographic data is absent. To address these issues, we developed "],"journal":["Scientific reports"],"pagination":["24306"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11484971"],"repository":["biostudies-literature"],"pubmed_title":["Geoinference of author affiliations using NLP-based text classification."],"pmcid":["PMC11484971"],"pubmed_authors":["Lee B","Brownstein JS","Kohane IS"],"additional_accession":[]},"is_claimable":false,"name":"Geoinference of author affiliations using NLP-based text classification.","description":"Author affiliations are essential in bibliometric studies, requiring relevant information extraction from free-text affiliations. Precisely determining an author's location from their affiliation is crucial for understanding research networks, collaborations, and geographic distribution. Existing geoparsing tools using regular expressions have limitations due to unstructured and ambiguous affiliations, resulting in erroneous location identification, especially for unconventional variations or misspellings. Moreover, their inefficient handling of big datasets hampers large-scale bibliometric studies. Though machine learning-based geoparsers exist, they depend on explicit location information, creating challenges when detailed geographic data is absent. To address these issues, we developed ","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Oct","modification":"2025-04-04T20:55:45.51Z","creation":"2025-04-04T20:55:45.51Z"},"accession":"S-EPMC11484971","cross_references":{"pubmed":["39414801"],"doi":["10.1038/s41598-024-73318-7"]}}