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 sev-

eral important applications such as extracting named enti-

ties from text documents and identifying biological concepts

from biomedical literature. In this paper, we develop a filter-

verification framework, and propose a novel in-memory fil-

ter 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 effciency and effectiveness of our filter over real datasets,

and show that it significantly outperforms the previous best-

known methods in terms of both filtering power and com-

putation time.

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In  SIGMOD Conference

Publisher  Association for Computing Machinery, Inc.
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