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Register a new userNeural Text Classification and Stacked Heterogeneous Embeddings for Named Entity Recognition in SMM4H 2021
تصنيف النص العصبي وإدارات غير متجانسة مكدسة للتعرف على الكيان المسمى في SMM4H 2021
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تقدم هذه الورقة نتائجنا من المشاركة في المهمة المشتركة SMM4H 2021. تناولنا التعرف على الكيان المسمى (NER) وتصنيف النص.لمعالجة NER، استكشفنا Bilstm-CRF مع تضمين مخلجان مكدسة وميزات لغوية.حققنا في العديد من خوارزميات التعلم في الآلات (الانحدار اللوجستي، SVM والشبكات العصبية) لمعالجة تصنيف النص.يمكن التعميم مناهجنا المقترحة لغات مختلفة وقد أظهرنا فعاليتها للغة الإنجليزية والإسبانية.حققت تقارير تصنيف النص لدينا أداء تنافسي مع درجة F1 0.46 و 0.90 على تصنيف ADE (المهمة 1A) وتصنيف المهنة (المهمة 7A) على التوالي.في حالة NER، سجلت عمليات التقديمات لدينا درجة F1 من 0.50 و 0.82 على اكتشاف ADE SPAN (المهمة 1B) والكشف عن المهنة (المهمة 7 ب) على التوالي.
This paper presents our findings from participating in the SMM4H Shared Task 2021. We addressed Named Entity Recognition (NER) and Text Classification. To address NER we explored BiLSTM-CRF with Stacked Heterogeneous embeddings and linguistic features. We investigated various machine learning algorithms (logistic regression, SVM and Neural Networks) to address text classification. Our proposed approaches can be generalized to different languages and we have shown its effectiveness for English and Spanish. Our text classification submissions have achieved competitive performance with F1-score of 0.46 and 0.90 on ADE Classification (Task 1a) and Profession Classification (Task 7a) respectively. In the case of NER, our submissions scored F1-score of 0.50 and 0.82 on ADE Span Detection (Task 1b) and Profession span detection (Task 7b) respectively.
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