We consider the problem of identifying sub-strings of input text strings that approximately match with some member of a potentially large dictionary. This problem arises in several important applications such as extracting named entities from text documents and identifying biological concepts from biomedical literature. In this paper, we develop a ﬁlterveriﬁcation framework, and propose a novel in-memory ﬁlter structure. That is, we ﬁrst quickly ﬁlter out sub-strings that cannot match with any dictionary member, and then verify the remaining sub-strings against the dictionary. Our method does not produce false negatives. We demonstrate the eﬃciency and eﬀectiveness of our ﬁlter over real datasets, and show that it signiﬁcantly outperforms the previous bestknown methods in terms of both ﬁltering power and computation time.