Name Entity Recognition (NER) labels sequences of words in a text which are the names of things, such as person and company names, or gene and protein names.
Neural nets require large scale dataset during training process. However, it is quite expensive to have the access to enough data size. One approach to deal with this issue is Data augmentation, which means increasing the number of data points.
Here we briefly introduce some common evaluation metrics in NER tasks, considering both extracted boundary and entities.
A note for NLP Interview.