Recent work in machine learning for information extraction has focused on two distinct sub-problems: the conventional problem of filling template slots from natural language text, and the problem of wrapper induction, learning simple extraction procedures (``wrappers'') for highly structured text such as Web pages produced by CGI scripts. For suitably regular domains, existing wrapper induction algorithms can efficiently learn wrappers that are simple and highly accurate, but the regularity bias of these algorithms makes them unsuitable for most conventional information extraction tasks. Boosting is a technique for improving the performance of a simple machine learning algorithm by repeatedly applying it to the training set with different example weightings. We describe an algorithm that learns simple, low-coverage wrapper-like extraction patterns, which we then apply to conventional information extraction problems using boosting. The result is BWI, a trainable information extraction system with a strong precision bias and F1 performance competitive with or better than a state-of-the-art techniques using hidden Markov models.