When using the scikit-learn library in Python, I can use the CountVectorizer to create ngrams of a desired length (e.g. 2 words) like so: from sklearn.metrics.
bspline
apache-directory
reagent
admin-on-rest
ansible
openkm
rating
case-insensitive
cypress-clock
traversable
findcontrol
webbrowser-control
audit-tables
wolfssl
user-feedback
mapstatetoprops
pinnacle-cart
angular-loopback
addressable-gem
pagemethods
xsbt-web-plugin
lumen-routing
jboss-logging
string-math
sap-query
directsound
concurrent-queue
nao-robot
vertx-redis-client
exasolution