  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, Support Vector Machine (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 (team:MIC-NLP) 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.
