Managing Déjà Vu: Collection Building for the Identification of Nonidentical Duplicate Documents

As online document collections continue to expand, both on the Web and in proprietary environments, the need for duplicate detection becomes more critical. Few users wish to retrieve search results consisting of sets of duplicate documents, whether identical duplicates or close variants. The goal of this work is to facilitate (a) investigations into the phenomenon of near duplicates and (b) algorithmic approaches to minimizing its deleterious effect on search results. Harnessing the expertise of both client-users and professional searchers, we establish principled methods to generate a test collection for identifying and handling nonidentical duplicate documents. We subsequently examine a flexible method of characterizing and comparing documents to permit the identification of near duplicates. This method has produced promising results following an extensive evaluation using a production-based test collection created by domain experts.