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📢 Seriously, We can't go with Big5 or other non structured descriptions to diverse large amount of characters 👨‍👩‍👦‍👦 from many books 📚. Instead, The factorization + open-psychometrics antonyms extracted from dialogues is a key 🔑  for automatic character profiling that purely relies on book content 📖. With that, happy to share delighted to share with you 🙌  more on this topic in YouTube video:
📢 Seriously, We can't go with Big5 or other non structured descriptions to diverse large amount of characters 👨‍👩‍👦‍👦 from many books 📚. Instead, The factorization + open-psychometrics antonyms extracted from dialogues is a key 🔑  for automatic character profiling that purely relies on book content 📖. With that, happy to share delighted to share with you 🙌  more on this topic in YouTube video:
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link
https://youtu.be/UQQsXfZyjjc
https://youtu.be/UQQsXfZyjjc
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🔑 From which you will find out:
🔑 From which you will find out:
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✅ How to perform book processing 📖 aimed at personalities extraction
✅ How to perform book processing 📖 aimed at personalities extraction
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✅ How to impute personalities 👨‍👩‍👦‍👦 and character network for deep learning 🤖
✅ How to impute personalities 👨‍👩‍👦‍👦 and character network for deep learning 🤖
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✅ How to evaluate 📊 advances / experiment findings 🧪
✅ How to evaluate 📊 advances / experiment findings 🧪
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Additional materials:
Additional materials:
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🌟 Github: 
🌟 Github: 
link
https://github.com/nicolay-r/book-persona-retriever
https://github.com/nicolay-r/book-persona-retriever
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📜 Paper: 
📜 Paper: 
link
https://www.dropbox.com/scl/fi/0c2axh97hadolwphgu7it/rusnachenko2024personality.pdf?rlkey=g2yyzv01th2rjt4o1oky0q8zc&st=omssztha&dl=1
https://www.dropbox.com/scl/fi/0c2axh97hadolwphgu7it/rusnachenko2024personality.pdf?rlkey=g2yyzv01th2rjt4o1oky0q8zc&st=omssztha&dl=1
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📙 Google-colab experiments: 
📙 Google-colab experiments: 
link
https://colab.research.google.com/github/nicolay-r/deep-book-processing/blob/master/parlai_gutenberg_experiments.ipynb
https://colab.research.google.com/github/nicolay-r/deep-book-processing/blob/master/parlai_gutenberg_experiments.ipynb
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🦜 Task: 
🦜 Task: 
link
https://github.com/nicolay-r/parlai_bookchar_task/tree/master
https://github.com/nicolay-r/parlai_bookchar_task/tree/master
📢 Seriously, We can't go with Big5 or other non structured descriptions to diverse large amount of characters 👨‍👩‍👦‍👦 from many books 📚. Instead, The factorization + open-psychometrics antonyms extracted from dialogues is a key 🔑  for automatic character profiling that purely relies on book content 📖. With that, happy to share delighted to share with you 🙌  more on this topic in YouTube video:

https://youtu.be/UQQsXfZyjjc

🔑 From which you will find out:
✅ How to perform book processing 📖 aimed at personalities extraction
✅ How to impute personalities 👨‍👩‍👦‍👦 and character network for deep learning 🤖
✅ How to evaluate 📊 advances / experiment findings 🧪

Additional materials:
🌟 Github: https://github.com/nicolay-r/book-persona-retriever
📜 Paper: https://www.dropbox.com/scl/fi/0c2axh97hadolwphgu7it/rusnachenko2024personality.pdf?rlkey=g2yyzv01th2rjt4o1oky0q8zc&st=omssztha&dl=1
📙 Google-colab experiments: https://colab.research.google.com/github/nicolay-r/deep-book-processing/blob/master/parlai_gutenberg_experiments.ipynb 
🦜 Task: https://github.com/nicolay-r/parlai_bookchar_task/tree/master
👀
John6666 bfuzzy1
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aiisthebest
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Rafael
aiisthebest
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Nicolay Rusnachenko
nicolay-r
user