Extracting adverse drug reactions and their context using sequence labelling ensembles in TAC2017
Table 1: The number of annotated mentions (group of tokens), number of tokens, and the average number of tokens per mention in the provided training data
['[BOLD] Entity class', '[BOLD] #mentions', '[BOLD] #tokens', '[BOLD] Avg. tk/mention']
The class distribution of annotated entities is imbalanced, where the majority of annotations were adverse drug reactions. On the other hand, some related entities had only a few annotations. Lack of data for certain related entities presented a challenge for developing named entity recognition systems based on machine learning.