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.
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Machado, J., Martins, B., Borbinha, J. (2010). 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). In: Peters, C., et al. Multilingual Information Access Evaluation I. Text Retrieval Experiments. CLEF 2009. Lecture Notes in Computer Science, vol 6241. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15754-7_10
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DOI: https://doi.org/10.1007/978-3-642-15754-7_10
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