Text Analysis Challenge: Detect Looted Art.
Help Automate Analysis, Flagging and Ranking of Museum Art Provenance Texts by the Probability of a Hidden History
The Question: How to sift through the millions of objects in museums to identify top priorities for intensive research by humans?
The Goal: Automatically Classify and Rank 70,000 art provenance texts by probability that further research will turn up a deliberately concealed history of looting, forced sale, theft or forgery.
The Challenge: Analyse texts quickly for Red Flags, quantify, detect patterns, classify, rank, and learn. Whatever it takes to produce a reliable list of top suspects
For this challenge several datasets will be provided.
1) DATASET:70,000 art provenance texts for analysis
2) DATASET: 1000 Red Flag Names
3) DATESET: 10 Key Words or Phrases
TRIAGE: You're the doctor and the texts are your patients! Who's in good health and who's sick? How sick? With what disease? What kind of tests and measurements can we perform on the texts to help us to reach a diagnosis? What kind of markers for should we look for? How to look for them?
See code at https://github.com/parisdata/GLAMhack2020
Worked on documentation
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