  Active learning is a popular methodology in text classification - known in
the legal domain as "predictive coding" or "Technology Assisted Review" or
"TAR" - due to its potential to minimize the required review effort to build
effective classifiers. In this study, we use extensive experimentation to
examine the impact of popular seed set selection strategies in active learning,
within a predictive coding exercise, and evaluate different active learning
strategies against well-researched continuous active learning strategies for
the purpose of determining efficient training methods for classifying large
populations quickly and precisely. We study how random sampling, keyword models
and clustering based seed set selection strategies combined together with
top-ranked, uncertain, random, recall inspired, and hybrid active learning
document selection strategies affect the performance of active learning for
predictive coding. We use the percentage of documents requiring review to reach
75% recall as the "benchmark" metric to evaluate and compare our approaches. In
most cases we find that seed set selection methods have a minor impact, though
they do show significant impact in lower richness data sets or when choosing a
top-ranked active learning selection strategy. Our results also show that
active learning selection strategies implementing uncertainty, random, or 75%
recall selection strategies has the potential to reach the optimum active
learning round much earlier than the popular continuous active learning
approach (top-ranked selection). The results of our research shed light on the
impact of active learning seed set selection strategies and also the
effectiveness of the selection strategies for the following learning rounds.
Legal practitioners can use the results of this study to enhance the
efficiency, precision, and simplicity of their predictive coding process.
