Table 2.

Details of Embeddings

#	Embedding name and source	Details
em1	Drug Chatter Twitter [16]	1B drug tweets from user timelines; window size 5 and dimension 400
em2	Glove [33]	840B tokens, 2.2 M vocab, cased, 300d vectors
em3	Twitter Word2vec Embeddings [34]	400 million Twitter tweets; Negative sampling; Skip-gram architecture; Window of 1; subsampling rate of 0.001; Vector size of 400
em4	glove.twitter.27B [33]	2B tweets, 27B tokens, 1.2 M vocab, uncased,200d vectors
em5	RedMed Model [31]	3 M tokens, 64d; Reddit drug posts
em6	Glove [33]	42B tokens, 1.9 M vocab, uncased, 300d vectors