Language Modeling for Information Retrieval
Language Modeling for Information Retrieval
BUAP-UPV TPIRS: a system for document indexing reduction at WebCLEF
CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
A comparison of methods for the automatic identification of locations in wikipedia
Proceedings of the 4th ACM workshop on Geographical information retrieval
A Competitive Term Selection Method for Information Retrieval
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
Using query-relevant documents pairs for cross-lingual information retrieval
TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
BUAP-UPV TPIRS: a system for document indexing reduction at WebCLEF
CLEF'05 Proceedings of the 6th international conference on Cross-Language Evalution Forum: accessing Multilingual Information Repositories
A penalisation-based ranking approach for the mixed monolingual task of WebCLEF 2006
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
Vocabulary reduction and text enrichment at WebCLEF
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
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In this paper we present the results of BUAP/UPV universities in WebCLEF, a particular task of CLEF 2005. Particularly, we evaluate our information retrieval system at the bilingual “English to Spanish” task. Our system uses a term reduction process based on the Transition Point technique. Our results show that it is possible to reduce the number of terms to index, thereby improving the performance of our system. We evaluate different percentages of reduction over a subset of EuroGOV, in order to determine the best one. We observed that after reducing the 82.55% of the corpus, a Mean Reciprocal Rank of 0.0844 was obtained, compared with 0.0465 of such evaluation with full documents.