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Register a new userLeveraging knowledge sources for detecting self-reports of particular health issues on social media
الاستفادة من مصادر المعرفة للكشف عن تقارير ذاتية عن قضايا صحية معينة بشأن وسائل التواصل الاجتماعي
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تحقق هذه الورقة في دمج مصادر المعرفة الجودة التي طورها خبراء المجال الطبي وكذلك المعلومات الأساسية لتصنيف التغريدات إلى أربع فئات مختلفة موجهة نحو الصحة.ندعي أن موارد مثل التسلسل الهرمي للشبكة والمعلومات المتاحة حاليا هي ملحقات فعالة من مجموعات بيانات التدريب المعتدلة لمختلف مهام تصنيف سقسقة الرحلات الدقيقة للمشكلات الصحية المبلغ عنها ذاتيا.
This paper investigates incorporating quality knowledge sources developed by experts for the medical domain as well as syntactic information for classification of tweets into four different health oriented categories. We claim that resources such as the MeSH hierarchy and currently available parse information are effective extensions of moderately sized training datasets for various fine-grained tweet classification tasks of self-reported health issues.
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We present machine learning classifiers to automatically identify COVID-19 misinformation on social media in three languages: English, Bulgarian, and Arabic. We compared 4 multitask learning models for this task and found that a model trained with En
glish BERT achieves the best results for English, and multilingual BERT achieves the best results for Bulgarian and Arabic. We experimented with zero shot, few shot, and target-only conditions to evaluate the impact of target-language training data on classifier performance, and to understand the capabilities of different models to generalize across languages in detecting misinformation online. This work was performed as a submission to the shared task, NLP4IF 2021: Fighting the COVID-19 Infodemic. Our best models achieved the second best evaluation test results for Bulgarian and Arabic among all the participating teams and obtained competitive scores for English.
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This paper describes the Helsinki--Ljubljana contribution to the VarDial 2021 shared task on social media variety geolocation. Following our successful participation at VarDial 2020, we again propose constrained and unconstrained systems based on the
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