  Named Entity Recognition (NER) and Relation Extraction (RE) are essential
tools in distilling knowledge from biomedical literature. This paper presents
our findings from participating in BioNLP Shared Tasks 2019. We addressed Named
Entity Recognition including nested entities extraction, Entity Normalization
and Relation Extraction. Our proposed approach of Named Entities can be
generalized to different languages and we have shown it's effectiveness for
English and Spanish text. We investigated linguistic features, hybrid loss
including ranking and Conditional Random Fields (CRF), multi-task objective and
token-level ensembling strategy to improve NER. We employed dictionary based
fuzzy and semantic search to perform Entity Normalization. Finally, our RE
system employed Support Vector Machine (SVM) with linguistic features.
  Our NER submission (team:MIC-CIS) ranked first in BB-2019 norm+NER task with
standard error rate (SER) of 0.7159 and showed competitive performance on
PharmaCo NER task with F1-score of 0.8662. Our RE system ranked first in the
SeeDev-binary Relation Extraction Task with F1-score of 0.3738.
