  We enhance the autonomy of the continuous active learning method shown by
Cormack and Grossman (SIGIR 2014) to be effective for technology-assisted
review, in which documents from a collection are retrieved and reviewed, using
relevance feedback, until substantially all of the relevant documents have been
reviewed. Autonomy is enhanced through the elimination of topic-specific and
dataset-specific tuning parameters, so that the sole input required by the user
is, at the outset, a short query, topic description, or single relevant
document; and, throughout the review, ongoing relevance assessments of the
retrieved documents. We show that our enhancements consistently yield superior
results to Cormack and Grossman's version of continuous active learning, and
other methods, not only on average, but on the vast majority of topics from
four separate sets of tasks: the legal datasets examined by Cormack and
Grossman, the Reuters RCV1-v2 subject categories, the TREC 6 AdHoc task, and
the construction of the TREC 2002 filtering test collection.
