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Mining Document Collections to Facilitate Accurate Approximate Entity Matching

Surajit Chaudhuri, Venkatesh Ganti, and Dong Xin

Abstract

Tasks recognizing named entities such as products, people names, or locations from documents have recently received significant attention in the literature. Many solutions to these tasks assume the existence of reference entity tables. An important challenge that needs to be addressed in the entity extraction task is that of ascertaining whether or not a candidate string approximately matches with a named entity in a given reference table.

Prior approaches have relied on string-based similarity which only compare a candidate string and an entity it matches with. In this paper, we exploit web search engines in order to define new similarity functions. We then develop efficient techniques to facilitate approximate matching in the context of our proposed similarity functions. In an extensive experimental evaluation, we demonstrate the accuracy and efficiency of our techniques.

Details

Publication typeInproceedings
Published inVLDB
PublisherVery Large Data Bases Endowment Inc.
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