  We study the problem of building entity tagging systems by using a few rules
as weak supervision. Previous methods mostly focus on disambiguation entity
types based on contexts and expert-provided rules, while assuming entity spans
are given. In this work, we propose a novel method TALLOR that bootstraps
high-quality logical rules to train a neural tagger in a fully automated
manner. Specifically, we introduce compound rules that are composed from simple
rules to increase the precision of boundary detection and generate more diverse
pseudo labels. We further design a dynamic label selection strategy to ensure
pseudo label quality and therefore avoid overfitting the neural tagger.
Experiments on three datasets demonstrate that our method outperforms other
weakly supervised methods and even rivals a state-of-the-art distantly
supervised tagger with a lexicon of over 2,000 terms when starting from only 20
simple rules. Our method can serve as a tool for rapidly building taggers in
emerging domains and tasks. Case studies show that learned rules can
potentially explain the predicted entities.
