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📢 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 🙌
🔍 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 🙌