Abstract. For the CLEF 2004 ImageCLEF St Andrew's Collection task the Dublin City University group carried out three sets of experiments: standard cross-language information retrieval (CLIR) runs using topic translation via machine translation (MT), combination of this run with image matching results from the VIPER system, and a novel document rescoring approach based on automatic MT evaluation metrics. Our standard MT-based CLIR works well on this task. Encouragingly combination with image matching lists is also observed to produce small positive changes in the retrieval output. However, rescoring using the MT evaluation metrics in their current form significantly reduced retrieval effectiveness.
Abstract. The Dublin City University group participated in the monolingual, bilingual and multilingual retrieval tasks. The main focus of our investigation for CLEF 2004 was extending our information retrieval system to document languages other than English, and completing the multilingual task comprising four languages: English, French, Russian and Finnish. Our retrieval system is based on the City University Okapi BM25 system with document preprocessing using the Snowball stemming software and stopword lists. Our French monolingual experiments compare retrieval using French documents and topics, and documents and topics translated into English. Our results indicate that working directly in French is more effective for retrieval than adopting document and topic translation. A breakdown of our multilingual retrieval results by the individual languages shows that similar overall average precision can be achieved when there is significant underlying variation in performance for individual languages.
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