Spelling suggestions: "subject:"reduplicate detection"" "subject:"deduplicate detection""
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The Impact of Near-Duplicate Documents on Information Retrieval EvaluationKhoshdel Nikkhoo, Hani 18 January 2011 (has links)
Near-duplicate documents can adversely affect the efficiency and
effectiveness of search engines.
Due to the pairwise nature of the comparisons required for near-duplicate
detection, this process is extremely costly in terms of the time and
processing power it requires.
Despite the ubiquitous presence of near-duplicate detection algorithms
in commercial search engines, their application and impact in research
environments is not fully explored.
The implementation of near-duplicate detection algorithms forces trade-offs
between efficiency and effectiveness, entailing careful testing and
measurement to ensure acceptable performance.
In this thesis, we describe and evaluate a scalable implementation of a
near-duplicate detection algorithm, based on standard shingling techniques,
running under a MapReduce framework.
We explore two different shingle sampling techniques and analyze
their impact on the near-duplicate document detection process.
In addition, we investigate the prevalence of near-duplicate documents
in the runs submitted to the adhoc task of TREC 2009 web track.
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The Impact of Near-Duplicate Documents on Information Retrieval EvaluationKhoshdel Nikkhoo, Hani 18 January 2011 (has links)
Near-duplicate documents can adversely affect the efficiency and
effectiveness of search engines.
Due to the pairwise nature of the comparisons required for near-duplicate
detection, this process is extremely costly in terms of the time and
processing power it requires.
Despite the ubiquitous presence of near-duplicate detection algorithms
in commercial search engines, their application and impact in research
environments is not fully explored.
The implementation of near-duplicate detection algorithms forces trade-offs
between efficiency and effectiveness, entailing careful testing and
measurement to ensure acceptable performance.
In this thesis, we describe and evaluate a scalable implementation of a
near-duplicate detection algorithm, based on standard shingling techniques,
running under a MapReduce framework.
We explore two different shingle sampling techniques and analyze
their impact on the near-duplicate document detection process.
In addition, we investigate the prevalence of near-duplicate documents
in the runs submitted to the adhoc task of TREC 2009 web track.
|
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Near-Duplicate Detection Using Instance Level ConstraintsPatel, Vishal 08 1900 (has links) (PDF)
For the task of near-duplicate document detection, comparison approaches based on bag-of-words used in information retrieval community are not sufficiently accurate. This work presents novel approach when instance-level constraints are given for documents and it is needed to retrieve them, given new query document for near-duplicate detection. The framework incorporates instance-level constraints and clusters documents into groups using novel clustering approach Grouped Latent Dirichlet Allocation (gLDA). Then distance metric is learned for each cluster using large margin nearest neighbor algorithm and finally ranked documents for given new unknown document using learnt distance metrics. The variety of experimental results on various datasets demonstrate that our clustering method (gLDA with side constraints) performs better than other clustering methods and the overall approach outperforms other near-duplicate detection algorithms.
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