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)

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 collection 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-Gram, with n between 2 and 5, using weight factors obtained by learning from last year topics and assessments. The rochio 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.