Top- k Ranked Document Search in General Text Databases
Identifieur interne : 000645 ( Main/Exploration ); précédent : 000644; suivant : 000646Top- k Ranked Document Search in General Text Databases
Auteurs : Shane Culpepper [Australie] ; Gonzalo Navarro [Chili] ; J. Puglisi [Australie] ; Andrew Turpin [Australie]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2010.
Abstract
Abstract: Text search engines return a set of k documents ranked by similarity to a query. Typically, documents and queries are drawn from natural language text, which can readily be partitioned into words, allowing optimizations of data structures and algorithms for ranking. However, in many new search domains (DNA, multimedia, OCR texts, Far East languages) there is often no obvious definition of words and traditional indexing approaches are not so easily adapted, or break down entirely. We present two new algorithms for ranking documents against a query without making any assumptions on the structure of the underlying text. We build on existing theoretical techniques, which we have implemented and compared empirically with new approaches introduced in this paper. Our best approach is significantly faster than existing methods in RAM, and is even three times faster than a state-of-the-art inverted file implementation for English text when word queries are issued.
Url:
DOI: 10.1007/978-3-642-15781-3_17
Affiliations:
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Typically, documents and queries are drawn from natural language text, which can readily be partitioned into words, allowing optimizations of data structures and algorithms for ranking. However, in many new search domains (DNA, multimedia, OCR texts, Far East languages) there is often no obvious definition of words and traditional indexing approaches are not so easily adapted, or break down entirely. We present two new algorithms for ranking documents against a query without making any assumptions on the structure of the underlying text. We build on existing theoretical techniques, which we have implemented and compared empirically with new approaches introduced in this paper. Our best approach is significantly faster than existing methods in RAM, and is even three times faster than a state-of-the-art inverted file implementation for English text when word queries are issued.</div></front></TEI><affiliations><list><country><li>Australie</li><li>Chili</li></country></list><tree><country name="Australie"><noRegion><name sortKey="Culpepper, Shane" sort="Culpepper, Shane" uniqKey="Culpepper S" first="Shane" last="Culpepper">Shane Culpepper</name></noRegion><name sortKey="Culpepper, Shane" sort="Culpepper, Shane" uniqKey="Culpepper S" first="Shane" last="Culpepper">Shane Culpepper</name><name sortKey="Puglisi, J" sort="Puglisi, J" uniqKey="Puglisi J" first="J." last="Puglisi">J. Puglisi</name><name sortKey="Puglisi, J" sort="Puglisi, J" uniqKey="Puglisi J" first="J." last="Puglisi">J. Puglisi</name><name sortKey="Turpin, Andrew" sort="Turpin, Andrew" uniqKey="Turpin A" first="Andrew" last="Turpin">Andrew Turpin</name><name sortKey="Turpin, Andrew" sort="Turpin, Andrew" uniqKey="Turpin A" first="Andrew" last="Turpin">Andrew Turpin</name></country><country name="Chili"><noRegion><name sortKey="Navarro, Gonzalo" sort="Navarro, Gonzalo" uniqKey="Navarro G" first="Gonzalo" last="Navarro">Gonzalo Navarro</name></noRegion><name sortKey="Navarro, Gonzalo" sort="Navarro, Gonzalo" uniqKey="Navarro G" first="Gonzalo" last="Navarro">Gonzalo Navarro</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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