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This paper presents our findings from participating in the SMM4H Shared Task2021. We addressed Named Entity Recognition (NER) and Text Classification. Toaddress NER we explored BiLSTM-CRF with Stacked Heterogeneous Embeddings andlinguistic features. We investigated various machine learning algorithms(logistic regression, Support Vector Machine (SVM) and Neural Networks) toaddress text classification. Our proposed approaches can be generalized todifferent languages and we have shown its effectiveness for English andSpanish. Our text classification submissions (team:MIC-NLP) have achievedcompetitive performance with F1-score of $0.46$ and $0.90$ on ADEClassification (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 ADESpan Detection (Task 1b) and Profession Span detection (Task 7b) respectively.

Neural Text Classification and Stacked Heterogeneous Embeddings for Named Entity Recognition in SMM4H 2021