SINAI experience at CLEF
نویسندگان
چکیده
منابع مشابه
SINAI experience at CLEF
This paper reports our work on CLEF and CrossLanguage Information Retrieval using CLEF resources. We aim to construct a highly languageindependent CLIR model. To accomplish this objective, several problems must be overcome: text translation or pseudo-translation and merging the obtained results for each language for a given query. Three issues of text-translation are investigated: the impact of...
متن کاملSINAI at CLEF eHealth 2017 Task 3
In this paper we present our participation as SINAI research group from the University of Jaén at Task3 Patient-Centred Information Retrieval. Although only two runs are allowed to be submitted, we have tried several strategies using different models and parameters in order to check the effectiveness of our system. The main 3 approaches try to apply query feedback using MeSH expansion, search e...
متن کاملSINAI at CLEF 2003: Decompounding and Merging
This paper describes the application of the two-step RSV and mixed two-step RSV merging methods over 8 and 4 multilingual tasks in CLEF 2003. We study their performance compared to previous studies and approaches. Furthermore, a new strategy for dealing with compound words is presented and evaluated within our methods, allowing automatic decomposition by using predefined vocabularies.
متن کاملSINAI at CL-SR Task at CLEF 2007
This paper describes the first participation of the SINAI team in the CLEF 2007 CLSR track. This year, we only want to establish a first contact with the task and the collections. Thus, we have pre-processed the collection using the Information Gain technique in order to filter the labels with most relevant information. We have used the LEMUR toolkit as the Information Retrieval system in our e...
متن کاملSINAI at QA@CLEF 2007. Answer Validation Exercise
This paper describes the rst participation of the SINAI (Intelligent Systems of Access Information) group of the University of Jaén in the AVE task of QA@CLEF 2007. We have developed a system made up of training and classi cation processes, that uses machine learning methods (bbr, timbl). Based on lexical features it obtains good results, a 41% of QA accuracy.
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ژورنال
عنوان ژورنال: INTELIGENCIA ARTIFICIAL
سال: 2004
ISSN: 1988-3064,1137-3601
DOI: 10.4114/ia.v8i22.809