Experiments with N-Gram Prefixes on a Multinomial Language Model versus Lucene’s Off-the-Shelf Ranking Scheme and Rocchio Query Expansion (TEL@CLEF Monolingual Task)
Identifieur interne : 000719 ( Main/Exploration ); précédent : 000718; suivant : 000720Experiments with N-Gram Prefixes on a Multinomial Language Model versus Lucene’s Off-the-Shelf Ranking Scheme and Rocchio Query Expansion (TEL@CLEF Monolingual Task)
Auteurs : Jorge Machado [Portugal] ; Bruno Martins [Portugal] ; José Borbinha [Portugal]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2010.
Abstract
Abstract: We describe our participation in the TEL@CLEF task of the CLEF 2009 ad-hoc track, where we measured the retrieval performance of LGTE, an index engine for Geo-Temporal collections which is mostly based on Lucene, together with extensions for query expansion and multinomial language modelling. We experiment an N-Gram stemming model to improve our last year experiments which consisted in combinations of query expansion, Lucene’s off-the-shelf ranking scheme and the ranking scheme based on multinomial language modeling. The N-Gram stemming model was based in a linear combination of N-Grams, with N between 2 and 5, using weight factors obtained by learning from last year topics and assessments. The Rocchio ranking function was also adapted to implement this N-Gram model. Results show that this stemming technique together with query expansion and multinomial language modeling both result in increased performance.
Url:
DOI: 10.1007/978-3-642-15754-7_10
Affiliations:
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We experiment an N-Gram stemming model to improve our last year experiments which consisted in combinations of query expansion, Lucene’s off-the-shelf ranking scheme and the ranking scheme based on multinomial language modeling. The N-Gram stemming model was based in a linear combination of N-Grams, with N between 2 and 5, using weight factors obtained by learning from last year topics and assessments. The Rocchio ranking function was also adapted to implement this N-Gram model. Results show that this stemming technique together with query expansion and multinomial language modeling both result in increased performance.</div></front></TEI><affiliations><list><country><li>Portugal</li></country></list><tree><country name="Portugal"><noRegion><name sortKey="Machado, Jorge" sort="Machado, Jorge" uniqKey="Machado J" first="Jorge" last="Machado">Jorge Machado</name></noRegion><name sortKey="Borbinha, Jose" sort="Borbinha, Jose" uniqKey="Borbinha J" first="José" last="Borbinha">José Borbinha</name><name sortKey="Borbinha, Jose" sort="Borbinha, Jose" uniqKey="Borbinha J" first="José" last="Borbinha">José Borbinha</name><name sortKey="Machado, Jorge" sort="Machado, Jorge" uniqKey="Machado J" first="Jorge" last="Machado">Jorge Machado</name><name sortKey="Martins, Bruno" sort="Martins, Bruno" uniqKey="Martins B" first="Bruno" last="Martins">Bruno Martins</name><name sortKey="Martins, Bruno" sort="Martins, Bruno" uniqKey="Martins B" first="Bruno" last="Martins">Bruno Martins</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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