content.raw: 📢 For those who tracking adances in Sentiment Analysis, this post might be relevant for you.  So far we arranged RuOpinionNE-2024 competition with final stage that has just been completed several days ago. Here the quick findings we got from the top submissions. 🎉 
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content.value: 📢 For those who tracking adances in Sentiment Analysis, this post might be relevant for you.  So far we arranged RuOpinionNE-2024 competition with final stage that has just been completed several days ago. Here the quick findings we got from the top submissions. 🎉 
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content.raw: 🔍 First, RuOpinonNE-2024 competition 💻 on extraction of opinion tuples (opinion source, opinion object, tonality, linguistic expression) from low resource domain news texts written in Russian language. The competition is hosted by codalab platform:
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content.value: 🔍 First, RuOpinonNE-2024 competition 💻 on extraction of opinion tuples (opinion source, opinion object, tonality, linguistic expression) from low resource domain news texts written in Russian language. The competition is hosted by codalab platform:
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content.href: https://codalab.lisn.upsaclay.fr/competitions/20244
content.raw: https://codalab.lisn.upsaclay.fr/competitions/20244
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content.raw: To asses the advanes, we adopt F1 over sentiment classes which also involves evalution of the spans.
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content.value: To asses the advanes, we adopt F1 over sentiment classes which also involves evalution of the spans.
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content.raw: 👏 Among 7 participants in total, the top three submissions showcase the following results:
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content.value: 👏 Among 7 participants in total, the top three submissions showcase the following results:
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content.raw: 🥉msuai F1=0.33 🎊
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content.value: 🥉msuai F1=0.33 🎊
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content.raw: 🥈RefalMachine showcase +0.02 F1=0.35 🎊
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content.value: 🥈RefalMachine showcase +0.02 F1=0.35 🎊
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content.raw: 🏆VatolinAlexey showcase +0.06 F1=0.41 🎊
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content.value: 🏆VatolinAlexey showcase +0.06 F1=0.41 🎊
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content.raw: 📝 At present, the competition organizers are working on:
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content.value: 📝 At present, the competition organizers are working on:
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content.raw: 1. 🟡 Collecting information about models utilized participants to contribute here with pre-trained models / concepts;
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content.value: 1. 🟡 Collecting information about models utilized participants to contribute here with pre-trained models / concepts;
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content.raw: 2. 🟡 Wrapping up findings from the submissions in a paper.
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content.value: 2. 🟡 Wrapping up findings from the submissions in a paper.
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content.raw: 🔔 For more information and further updates, the most complete source that complements codalab is this github:
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content.value: 🔔 For more information and further updates, the most complete source that complements codalab is this github:
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content.href: https://github.com/dialogue-evaluation/RuOpinionNE-2024
content.raw: https://github.com/dialogue-evaluation/RuOpinionNE-2024
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content.raw: The RuOpinionNE-2024 are now in post-evaluation stage, so everyone interested in low resouce domain evaluation on opinon extraction are welcome 🙌
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content.value: The RuOpinionNE-2024 are now in post-evaluation stage, so everyone interested in low resouce domain evaluation on opinon extraction are welcome 🙌
rawContent: 📢 For those who tracking adances in Sentiment Analysis, this post might be relevant for you.  So far we arranged RuOpinionNE-2024 competition with final stage that has just been completed several days ago. Here the quick findings we got from the top submissions. 🎉 
  
  🔍 First, RuOpinonNE-2024 competition 💻 on extraction of opinion tuples (opinion source, opinion object, tonality, linguistic expression) from low resource domain news texts written in Russian language. The competition is hosted by codalab platform:
  
  https://codalab.lisn.upsaclay.fr/competitions/20244
  
  To asses the advanes, we adopt F1 over sentiment classes which also involves evalution of the spans.
  👏 Among 7 participants in total, the top three submissions showcase the following results:
  
  🥉msuai F1=0.33 🎊
  🥈RefalMachine showcase +0.02 F1=0.35 🎊
  🏆VatolinAlexey showcase +0.06 F1=0.41 🎊
  
  📝 At present, the competition organizers are working on:
  1. 🟡 Collecting information about models utilized participants to contribute here with pre-trained models / concepts;
  2. 🟡 Wrapping up findings from the submissions in a paper.
  
  🔔 For more information and further updates, the most complete source that complements codalab is this github:
  https://github.com/dialogue-evaluation/RuOpinionNE-2024
  
  The RuOpinionNE-2024 are now in post-evaluation stage, so everyone interested in low resouce domain evaluation on opinon extraction are welcome 🙌
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