Finally, we evaluated the F1 score of different labels individually to identify which type of entity is more difficult for the model to identify.
final representation of a word is obtained by concatenating the left context − → h t and right context We use 150dimensional LSTM in forward and backward direction so that, a 300-dimensional vector representation is learned for each word.