  For several purposes in Natural Language Processing (NLP), such as
Information Extraction, Sentiment Analysis or Chatbot, Named Entity Recognition
(NER) holds an important role as it helps to determine and categorize entities
in text into predefined groups such as the names of persons, locations,
quantities, organizations or percentages, etc. In this report, we present our
experiments on a neural architecture composed of a Conditional Random Field
(CRF) layer stacked on top of a Bi-directional LSTM (BI-LSTM) layer for solving
NER tasks. Besides, we also employ a fusion input of embedding vectors (Glove,
BERT), which are pre-trained on the huge corpus to boost the generalization
capacity of the model. Unfortunately, due to the heavy unbalanced distribution
cross-training data, both approaches just attained a bad performance on less
training samples classes. To overcome this challenge, we introduce an add-on
classification model to split sentences into two different sets: Weak and
Strong classes and then designing a couple of Bi-LSTM-CRF models properly to
optimize performance on each set. We evaluated our models on the test set and
discovered that our method can improve performance for Weak classes
significantly by using a very small data set (approximately 0.45\%) compared to
the rest classes.
