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📢 For those who are interested in extracting information about ✍️ authors from texts, happy to share personal 📹  on Reading Between the lines: adapting ChatGPT-related systems 🤖  for Implicit Information Retrieval National 
📢 For those who are interested in extracting information about ✍️ authors from texts, happy to share personal 📹  on Reading Between the lines: adapting ChatGPT-related systems 🤖  for Implicit Information Retrieval National 
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Youtube: 
Youtube: 
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https://youtu.be/nXClX7EDYbE
https://youtu.be/nXClX7EDYbE
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🔑 In this talk, we refer to IIR as such information that is indirectly expressed by ✍️ author / 👨 character / patient / any other entity.
🔑 In this talk, we refer to IIR as such information that is indirectly expressed by ✍️ author / 👨 character / patient / any other entity.
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📊  I cover the 1️⃣  pre-processing and 2️⃣  reasoning techniques, aimed at enhancing gen AI capabilities in IIR. To showcase the effectiveness of the proposed techniques, we experiment with such IIR tasks as Sentiment Analysis, Emotion Extraction / Causes Prediction.
📊  I cover the 1️⃣  pre-processing and 2️⃣  reasoning techniques, aimed at enhancing gen AI capabilities in IIR. To showcase the effectiveness of the proposed techniques, we experiment with such IIR tasks as Sentiment Analysis, Emotion Extraction / Causes Prediction.
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In pictures below, sharing the quick takeaways on the pipeline construction and experiment results 🧪
In pictures below, sharing the quick takeaways on the pipeline construction and experiment results 🧪
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Related paper cards:
Related paper cards:
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📜 emotion-extraction: 
📜 emotion-extraction: 
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https://nicolay-r.github.io/#semeval2024-nicolay
https://nicolay-r.github.io/#semeval2024-nicolay
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📜 sentiment-analysis: 
📜 sentiment-analysis: 
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https://nicolay-r.github.io/#ljom2024
https://nicolay-r.github.io/#ljom2024
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Models:
Models:
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https://huggingface.co/nicolay-r/flan-t5-tsa-thor-base
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nicolay-r/flan-t5-tsa-thor-base
https://huggingface.co/nicolay-r/flan-t5-tsa-thor-base
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https://huggingface.co/nicolay-r/flan-t5-emotion-cause-thor-base
model
nicolay-r/flan-t5-emotion-cause-thor-base
https://huggingface.co/nicolay-r/flan-t5-emotion-cause-thor-base
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📓 PS: I got a hoppy for advetising HPMoR  ✨ 😁 
📓 PS: I got a hoppy for advetising HPMoR  ✨ 😁 
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📢 For those who are interested in extracting information about ✍️ authors from texts, happy to share personal 📹  on Reading Between the lines: adapting ChatGPT-related systems 🤖  for Implicit Information Retrieval National 

Youtube: https://youtu.be/nXClX7EDYbE

🔑 In this talk, we refer to IIR as such information that is indirectly expressed by ✍️ author / 👨 character / patient / any other entity.
 
📊  I cover the 1️⃣  pre-processing and 2️⃣  reasoning techniques, aimed at enhancing gen AI capabilities in IIR. To showcase the effectiveness of the proposed techniques, we experiment with such IIR tasks as Sentiment Analysis, Emotion Extraction / Causes Prediction.

In pictures below, sharing the quick takeaways on the pipeline construction and experiment results 🧪

Related paper cards:
📜 emotion-extraction: https://nicolay-r.github.io/#semeval2024-nicolay
📜 sentiment-analysis: https://nicolay-r.github.io/#ljom2024

Models:
https://huggingface.co/nicolay-r/flan-t5-tsa-thor-base
https://huggingface.co/nicolay-r/flan-t5-emotion-cause-thor-base


📓 PS: I got a hoppy for advetising HPMoR  ✨ 😁 

👀
John6666