  Distributed representations of text can be used as features when training a
statistical classifier. These representations may be created as a composition
of word vectors or as context-based sentence vectors. We compare the two kinds
of representations (word versus context) for three classification problems:
influenza infection classification, drug usage classification and personal
health mention classification. For statistical classifiers trained for each of
these problems, context-based representations based on ELMo, Universal Sentence
Encoder, Neural-Net Language Model and FLAIR are better than Word2Vec, GloVe
and the two adapted using the MESH ontology. There is an improvement of 2-4% in
the accuracy when these context-based representations are used instead of
word-based representations.
