impresso/named-entity-tutorial-dh2019
Tutorial on NE processing for Digital Humanities - DH Utrech 2019
This tutorial helps Digital Humanists and researchers working with historical texts to automatically identify key entities like people, locations, and organizations. It takes digitized historical documents or archives as input and outputs structured information about the named entities within them. Anyone analyzing large collections of historical documents to extract specific data points or build historical knowledge graphs would find this useful.
No commits in the last 6 months.
Use this if you need to extract and categorize named entities from large volumes of historical documents, such as old newspapers or cultural institution publications.
Not ideal if your primary need is to process contemporary, well-structured news text or very specific, niche domains outside of historical archives.
Stars
24
Forks
4
Language
Jupyter Notebook
License
—
Category
Last pushed
Jul 18, 2019
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/nlp/impresso/named-entity-tutorial-dh2019"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
hellohaptik/chatbot_ner
chatbot_ner: Named Entity Recognition for chatbots.
openeventdata/mordecai
Full text geoparsing as a Python library
Rostlab/nalaf
NLP framework in python for entity recognition and relationship extraction
mpuig/spacy-lookup
Named Entity Recognition based on dictionaries
NorskRegnesentral/skweak
skweak: A software toolkit for weak supervision applied to NLP tasks