  We tackle the task of Term Set Expansion (TSE): given a small seed set of
example terms from a semantic class, finding more members of that class. The
task is of great practical utility, and also of theoretical utility as it
requires generalization from few examples. Previous approaches to the TSE task
can be characterized as either distributional or pattern-based. We harness the
power of neural masked language models (MLM) and propose a novel TSE algorithm,
which combines the pattern-based and distributional approaches. Due to the
small size of the seed set, fine-tuning methods are not effective, calling for
more creative use of the MLM. The gist of the idea is to use the MLM to first
mine for informative patterns with respect to the seed set, and then to obtain
more members of the seed class by generalizing these patterns. Our method
outperforms state-of-the-art TSE algorithms. Implementation is available at:
https://github.com/ guykush/TermSetExpansion-MPB/
