NDLI: Query by document via a decomposition-based two-level retrieval approach

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Query by document via a decomposition-based two-level retrieval approach

Content Provider	ACM Digital Library
Author	Cai, Rui Weng, Linkai Yang, Laurence T. Zhang, Lei Li, Zhiwei Zhang, Yaoxue Zhou, Yuezhi
Abstract	Retrieving similar documents from a large-scale text corpus according to a given document is a fundamental technique for many applications. However, most of existing indexing techniques have difficulties to address this problem due to special properties of a document query, e.g. high dimensionality, sparse representation and semantic concern. Towards addressing this problem, we propose a two-level retrieval solution based on a document decomposition idea. A document is decomposed to a compact vector and a few document specific keywords by a dimension reduction approach. The compact vector embodies the major semantics of a document, and the document specific keywords complement the discriminative power lost in dimension reduction process. We adopt locality sensitive hashing (LSH) to index the compact vectors, which guarantees to quickly find a set of related documents according to the vector of a query document. Then we re-rank documents in this set by their document specific keywords. In experiments, we obtained promising results on various datasets in terms of both accuracy and performance. We demonstrated that this solution is able to index large-scale corpus for efficient similarity-based document retrieval.
Starting Page	505
Ending Page	514
Page Count	10
File Format	PDF
ISBN	9781450307574
DOI	10.1145/2009916.2009985
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-07-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Document decomposition Indexing Similarity search
Content Type	Text
Resource Type	Article

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