Extracting structured information from TXT files

TXT_replaceWORDwithREGEX.py

• adding delimiters to text based on regular expression
• preparing text for splitting into individual sections

TXT_splitUPPERCASE.py

• identifiying person entries based on uppercase characters in names

UniversityRecordsMainz_identifyPLACEofORIGIN.py

• identifying places of origin according to token position in text
• sample output of script for the Mainz university records: UniversityRecordsMainz_output_place-names.txt

TransferPROFData

• Transfer of semi-structured text data extracted via OCR from the Mainz university registers (originally written with a typewriter) to EXCEL
• Splitting the information into “name”, “information” and “source citation” columns
• Further refinement of the entries by matching the “information” with ontology lists
• Identification of event names, titles, functions, place names and dates

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The following blog post describes the application of some of the above-mentioned scripts in the “Kurmainz” work package:
Monika Barget, “Disambiguating people and places in dirty historical data,” in INSULAE, last updated 26/10/2021