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1
Overview of the CLEF eHealth Evaluation Lab 2019
Kelly, Liadh; Suominen, Hanna; Goeuriot, Lorraine. - : Springer Verlag, 2019
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2
Comparative Study of Monolingual and Multilingual Search Models for Use with Asian Languages
In: http://members.unine.ch/jacques.savoy/papers/clirtalip.pdf (2005)
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3
Report on CLEF-2005 evaluation campaign: Monolingual, bilingual, and GIRT information retrieval
In: http://clef.isti.cnr.it/2005/working_notes/workingnotes2005/savoy05.pdf (2005)
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4
Report on CLEF-2003 multilingual tracks
In: http://www.unine.ch/info/Gi/Papers/CLEF2003Multi.pdf (2004)
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5
Selection and Merging Strategies for Multilingual Information Retrieval
In: https://doc.rero.ch/record/17192/files/Savoy_Jacques_-_Selection_and_Merging_Strategies_for_Multilingual_20100211.pdf (2004)
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6
Combining Multiple Strategies for Effective Monolingual and Cross-Language Retrieval
In: https://doc.rero.ch/record/13167/files/Savoy_Jacques_-_Combining_Multiple_Strategies_for_Effective_20091208.pdf (2004)
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7
Report on clef-2002 experiments: Combining multiple sources of evidence
In: http://www.clef-campaign.org/workshop2002/WN/3.pdf (2002)
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8
Report on clef-2002 experiments: Combining multiple sources of evidence
In: http://members.unine.ch/jacques.savoy/papers/clef2002rv.pdf (2002)
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9
Report on CLEF-2001 Experiments
In: http://www.ercim.org/publication/ws-proceedings/CLEF2/savoy.pdf (2001)
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10
Report on CLEF-2001 Experiments
In: http://www.unine.ch/info/Gi/Papers/CLEF2001.pdf (2001)
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11
Domain-Specific IR for German, English and Russian Languages
In: http://www.clef-campaign.org/2007/working_notes/FautschCLEF2007.pdf
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12
UniNE at CLEF 2006: Experiments with Monolingual, Bilingual, Domain- Specific and Robust Retrieval
In: http://members.unine.ch/jacques.savoy/papers/clef2006wp.pdf
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13
Searching Strategies for the Hungarian Language
In: http://members.unine.ch/jacques.savoy/papers/huipm.pdf
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14
Lexical Analysis of Obama's and McCain's Speeches
In: http://members.unine.ch/jacques.savoy/Papers/USspeeches.pdf
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15
UniNE at FIRE 2010: Hindi, Bengali, and Marathi IR
In: http://www.isical.ac.in/~fire/paper_2010/Dolamic-UniNE-fire2010.pdf
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16
Stemming Approaches for East European Languages
In: http://clef.isti.cnr.it/2007/working_notes/DolamicCLEF2007.pdf
Abstract: In our participation in this CLEF evaluation campaign, the first objective is to propose and evaluate various indexing and search strategies for the Czech language in order to hopefully produce better retrieval effectiveness than that of the language-independent approach (n-gram). Based on our stemming strategy used with other languages, we propose two light stemmers for this Slavic language and a third one based on a more aggressive suffix-stripping scheme that removes some derivational suffixes. Our second objective is to obtain a better picture of the relative merit of various search engines in exploring Hungarian and Bulgarian documents. Moreover for the Bulgarian language we developed a new and more aggressive stemmer. To evaluate these solutions we use our various IR models, including the Okapi, Divergence from Randomness (DFR) and statistical language model (LM) together with the classical tf.idf vector-processing approach. Our experiments tend to show that for the Bulgarian language removing certain frequently used derivational suffixes may improve mean average precision. For the Hungarian corpus, applying an automatic decompounding procedure improves the MAP. For the Czech language, a comparison between a light (inflectional only) and a more aggressive stemmer that removes both inflectional and some derivational suffixes reveals small performance differences. For this language only, the performance difference between a word-based or a 4-gram indexing strategy is also rather small, while for the Hungarian or Bulgarian corpora, a word-based approach tend to produce better MAP.
Keyword: Algorithms. Additional Keywords and Phrases Natural Language Processing with East European Languages; Bulgarian Language; Categories and Subject Descriptors H.3.1 [Content Analysis and Indexing; Czech Language; Hungarian Language; Indexing methods; Language models. H.3.3 [Information Storage and Retrieval; Linguistic processing. I.2.7 [Natural Language Processing; Measurement; Performance; Performance evaluation. General Terms Experimentation; Retrieval models. H.3.4 [Systems and Software; Stemmer; Stemming Strategy
URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.584.5527
http://clef.isti.cnr.it/2007/working_notes/DolamicCLEF2007.pdf
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17
UniNE at CLEF 2009: Persian Ad Hoc Retrieval and IP
In: http://ceur-ws.org/Vol-1175/CLEF2009wn-Miscellaneous-DolamicEt2009.pdf
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18
S.: UniNE at CLEF 2006: Experiments with Monolingual, Bilingual, Domain-Specific and Robust Retrieval
In: http://www.samir-abdou.net/papers/savoyCLEF2006.pdf
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19
Multilingual Information Retrieval with Asian Languages
In: http://www3.riao.org/Proceedings-2004/papers/0050.pdf
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20
UniNE at CLEF 2008: TEL, Persian and Robust IR,” in Carol Peters et al. (Eds.): Evaluating Systems for Multilingual and
In: http://clef.isti.cnr.it/2008/working_notes/Dolamic-paperCLEF2008.pdf
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