Kaushik Chakrabarti, Surajit Chaudhuri, Venkatesh Ganti, and Dong Xin
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.
|Published in||SIGMOD Conference|
|Publisher||Association for Computing Machinery, Inc.|
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