Here contain the scripts and code used in ACL-BioNLP 2016 paper:
How to Train good Word Embeddings for Biomedical NLP

word2vec: original word2vec from Mikolov (https://code.google.com/archive/p/word2vec/)
wvlib: lib to read word2vec file (https://github.com/spyysalo/wvlib)
geniass: lib to segment bioMedical text (http://www.nactem.ac.uk/y-matsu/geniass/)

pre-process.sh: segment and tokenized input text (e.g. raw PubMed or PMC text)
create_shf_low_text.sh: create lowercased and sentence-shuffled text (input: tokenized text)
createModel.sh: Create word2vec.bin file with different parameters
intrinsicEva.sh: run intrinsic evaluation on UMNSRS and Mayo data-set (input: Dir. for testing vector)
ExtrinsicEva.sh: run extrinsic evaluation

Pre-processing:
tokenize_text.py: tokenized text (need NLTK installed)
geniass: segment sentence

Intrinsic evaluation:
evaluate.py: perform intrinisic evaluation

Extrinsic evaluation: (Keras folder: Need either tensorflow or theano installed):
mlp.py: simple feed-forward Neural Network
setting.py: parameters for the Neual Network