📢 This year I made decent amout of experiments on LLM reasoning capabilities in author opinion extraction. 
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📢 This year I made decent amout of experiments on LLM reasoning capabilities in author opinion extraction. 
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However, they did not go further with:
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However, they did not go further with:
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↗️ annoation of other sources of opinion causes: entities, out-of-context object (None).
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↗️ annoation of other sources of opinion causes: entities, out-of-context object (None).
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📏 evaluation of factual statements that support the extracted sentiment.
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📏 evaluation of factual statements that support the extracted sentiment.
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To address these limitations, so far we launch 🚀 RuOpinionNE-2024 competition on the Codalab platform: 
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To address these limitations, so far we launch 🚀 RuOpinionNE-2024 competition on the Codalab platform: 
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📊 
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📊 
https://codalab.lisn.upsaclay.fr/competitions/20244
https://codalab.lisn.upsaclay.fr/competitions/20244
link
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 The competition is aimed at extraction of opinion tuples (see attached images) from texts written in Russian.
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 The competition is aimed at extraction of opinion tuples (see attached images) from texts written in Russian.
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It proceeds the past RuSentNE-2023 codalab competition findings:
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It proceeds the past RuSentNE-2023 codalab competition findings:
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🔎 Past year competition: 
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🔎 Past year competition: 
https://www.dialog-21.ru/media/5896/golubevaplusetal118.pdf
https://www.dialog-21.ru/media/5896/golubevaplusetal118.pdf
link
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🔎 LLM reasoning 🧠: 
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🔎 LLM reasoning 🧠: 
https://arxiv.org/abs/2404.12342
https://arxiv.org/abs/2404.12342
link
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For those who interested to adopt Generative AI, the complete information about competition is below:
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For those who interested to adopt Generative AI, the complete information about competition is below:
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📊 RuOpinionNE-2024: 
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📊 RuOpinionNE-2024: 
https://codalab.lisn.upsaclay.fr/competitions/20244
https://codalab.lisn.upsaclay.fr/competitions/20244
link
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🗒 Task description: 
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🗒 Task description: 
https://codalab.lisn.upsaclay.fr/competitions/20244#learn_the_details-overview
https://codalab.lisn.upsaclay.fr/competitions/20244#learn_the_details-overview
link
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🔔 To follow updates: 
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🔔 To follow updates: 
https://t.me/RuOpinionNE2024
https://t.me/RuOpinionNE2024
link
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⏰ Stages Deadlines (might be extended)
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⏰ Stages Deadlines (might be extended)
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📦 Submission details (bottom of the competition page)
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📦 Submission details (bottom of the competition page)
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🙋 For questions you can contact 
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🙋 For questions you can contact 
@nicolay-r
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nicolay-r
: 
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: 
https://nicolay-r.github.io/
https://nicolay-r.github.io/
link
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🧪  Most recent findings on LLM application: 
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🧪  Most recent findings on LLM application: 
https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
https://github.com/nicolay-r/RuSentNE-LLM-Benchmark
link
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📢 This year I made decent amout of experiments on LLM reasoning capabilities in author opinion extraction. 
However, they did not go further with:
↗️ annoation of other sources of opinion causes: entities, out-of-context object (None).
📏 evaluation of factual statements that support the extracted sentiment.

To address these limitations, so far we launch 🚀 RuOpinionNE-2024 competition on the Codalab platform: 
📊 https://codalab.lisn.upsaclay.fr/competitions/20244

 The competition is aimed at extraction of opinion tuples (see attached images) from texts written in Russian.
It proceeds the past RuSentNE-2023 codalab competition findings:
🔎 Past year competition: https://www.dialog-21.ru/media/5896/golubevaplusetal118.pdf
🔎 LLM reasoning 🧠: https://arxiv.org/abs/2404.12342

For those who interested to adopt Generative AI, the complete information about competition is below:
📊 RuOpinionNE-2024: https://codalab.lisn.upsaclay.fr/competitions/20244
🗒 Task description: https://codalab.lisn.upsaclay.fr/competitions/20244#learn_the_details-overview
🔔 To follow updates: https://t.me/RuOpinionNE2024
⏰ Stages Deadlines (might be extended)
📦 Submission details (bottom of the competition page)

🙋 For questions you can contact @nicolay-r: https://nicolay-r.github.io/
🧪  Most recent findings on LLM application: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark

👀
John6666