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π’ For those who are interested in extracting information about βοΈ authors from texts, happy to share personal πΉ on Reading Between the lines: adapting ChatGPT-related systems π€ for Implicit Information Retrieval National
Youtube: https://youtu.be/nXClX7EDYbE
π In this talk, we refer to IIR as such information that is indirectly expressed by βοΈ author / π¨ character / patient / any other entity.
π I cover the 1οΈβ£ pre-processing and 2οΈβ£ 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 π§ͺ
Related paper cards:
π emotion-extraction: https://nicolay-r.github.io/#semeval2024-nicolay
π sentiment-analysis: https://nicolay-r.github.io/#ljom2024
Models:
nicolay-r/flan-t5-tsa-thor-base
nicolay-r/flan-t5-emotion-cause-thor-base
π PS: I got a hoppy for advetising HPMoR β¨ π
Youtube: https://youtu.be/nXClX7EDYbE
π In this talk, we refer to IIR as such information that is indirectly expressed by βοΈ author / π¨ character / patient / any other entity.
π I cover the 1οΈβ£ pre-processing and 2οΈβ£ 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 π§ͺ
Related paper cards:
π emotion-extraction: https://nicolay-r.github.io/#semeval2024-nicolay
π sentiment-analysis: https://nicolay-r.github.io/#ljom2024
Models:
nicolay-r/flan-t5-tsa-thor-base
nicolay-r/flan-t5-emotion-cause-thor-base
π PS: I got a hoppy for advetising HPMoR β¨ π