In this paper, we describe a new method for the problem of named entity classifica-tion for specialized or technical domains, using distant supervision. Our approach relies on a simple observation: in some specialized domains, named entities are almost unambiguous. Thus, given a seed list of names of entities, it is cheap and easy to obtain positive examples from unlabeled texts using a simple string match. Those positive examples can then be used to train a named entity classifier, by using the PU learning paradigm, which is learning from positive and unlabeled examples. We introduce a new convex formulation to solve this problem, and apply our technique in order to extract named entities from financial reports cor-responding to healthcare companies.