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Just Ask! Evaluating Machine Translation by Asking and Answering Questions ...
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2
Extended CLEF eHealth 2013-2015 IR Test Collection
Pecina, Pavel; Saleh, Shadi. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2019
BASE
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3
Czech Malach Cross-lingual Speech Retrieval Test Collection
Galuščáková, Petra; Pecina, Pavel; Hoffmannová, Petra. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2017
BASE
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4
Khresmoi Summary Translation Test Data 2.0
Dušek, Ondřej; Hajič, Jan; Hlaváčová, Jaroslava. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2017
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5
Khresmoi Query Translation Test Data 2.0
Pecina, Pavel; Dušek, Ondřej; Hajič, Jan. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2017
BASE
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6
CLEF 2017 Task overview: The IR Task at the eHealth Evaluation Lab evaluating retrieval methods for consumer Health search
Palotti, Joao; Zuccon, Guido; Jimmy. - : CEUR-WS, 2017
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7
The IR Task at the CLEF eHealth evaluation lab 2016: User-centred health information retrieval
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8
Domain adaptation of statistical machine translation with domain-focused web crawling [<Journal>]
Pecina, Pavel [Verfasser]; Toral, Antonio [Verfasser]; Papavassiliou, Vassilis [Verfasser].
DNB Subject Category Language
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9
ShARe/CLEF eHealth evaluation lab 2014, task 3: user-centred health information retrieval
In: Goeuriot, Lorraine orcid:0000-0001-7491-1980 , Kelly, Liadh orcid:0000-0003-1131-5238 , Li, Wei B. orcid:0000-0001-7347-3501 , Palotti, Joao, Pecina, Pavel, Zuccon, Guido, Hanbury, Allan, Jones, Gareth J.F. orcid:0000-0002-4033-9135 and Mueller, Henning (2014) ShARe/CLEF eHealth evaluation lab 2014, task 3: user-centred health information retrieval. In: Conference and Labs for the Evaluation Forum (CLEF 2014), 15-18 Sept 2014, Sheffield, UK. (2014)
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10
Adaptation of machine translation for multilingual information retrieval in the medical domain
In: Pecina, Pavel, Dušek, Ondřej, Goeuriot, Lorraine orcid:0000-0001-7491-1980 , Hajič, Jan, Hlaváčová, Jaroslava, Jones, Gareth J.F. orcid:0000-0002-4033-9135 , Kelly, Liadh orcid:0000-0003-1131-5238 , Leveling, Johannes orcid:0000-0003-0603-4191 , Mareček, David, Novák, Michal, Popel, Martin, Rosa, Rudolf, Tamchyna, Aleš and Urešová, Zdeňka (2014) Adaptation of machine translation for multilingual information retrieval in the medical domain. Artificial Intelligence in Medicine, 61 (3). pp. 165-185. ISSN 1873-2860 (2014)
Abstract: Objective. We investigate machine translation (MT) of user search queries in the context of cross-lingual information retrieval (IR) in the medical domain. The main focus is on techniques to adapt MT to increase translation quality; however, we also explore MT adaptation to improve eectiveness of cross-lingual IR. Methods and Data. Our MT system is Moses, a state-of-the-art phrase-based statistical machine translation system. The IR system is based on the BM25 retrieval model implemented in the Lucene search engine. The MT techniques employed in this work include in-domain training and tuning, intelligent training data selection, optimization of phrase table configuration, compound splitting, and exploiting synonyms as translation variants. The IR methods include morphological normalization and using multiple translation variants for query expansion. The experiments are performed and thoroughly evaluated on three language pairs: Czech–English, German–English, and French–English. MT quality is evaluated on data sets created within the Khresmoi project and IR eectiveness is tested on the CLEF eHealth 2013 data sets. Results. The search query translation results achieved in our experiments are outstanding – our systems outperform not only our strong baselines, but also Google Translate and Microsoft Bing Translator in direct comparison carried out on all the language pairs. The baseline BLEU scores increased from 26.59 to 41.45 for Czech–English, from 23.03 to 40.82 for German–English, and from 32.67 to 40.82 for French–English. This is a 55% improvement on average. In terms of the IR performance on this particular test collection, a significant improvement over the baseline is achieved only for French–English. For Czech–English and German–English, the increased MT quality does not lead to better IR results. Conclusions. Most of the MT techniques employed in our experiments improve MT of medical search queries. Especially the intelligent training data selection proves to be very successful for domain adaptation of MT. Certain improvements are also obtained from German compound splitting on the source language side. Translation quality, however, does not appear to correlate with the IR performance – better translation does not necessarily yield better retrieval. We discuss in detail the contribution of the individual techniques and state-of-the-art features and provide future research directions.
Keyword: Information retrieval; Machine translating; Medical information retrieval
URL: http://doras.dcu.ie/20117/
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11
ShARe/CLEF eHealth Evaluation Lab 2014, Task 3: User-centred health information retrieval
In: Proceedings of CLEF 2014 ; https://hal.archives-ouvertes.fr/hal-01086554 ; Proceedings of CLEF 2014, Sep 2014, Sheffield, United Kingdom (2014)
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12
Adaptation of machine translation for multilingual information retrieval in the medical domain
In: ISSN: 0933-3657 ; Artificial Intelligence in Medicine ; https://hal.archives-ouvertes.fr/hal-01921881 ; Artificial Intelligence in Medicine, Elsevier, 2014, 61 (3), pp.165 - 185. &#x27E8;10.1016/j.artmed.2014.01.004&#x27E9; (2014)
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13
Gold Standard Reference Data for Multiword Expression Extraction: Czech Dependency Bigrams from the Prague Dependency Treebank
Pecina, Pavel. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2014
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14
Moses Web Demo
Bojar, Ondřej; Cífka, Ondřej; Pecina, Pavel. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2014
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15
Khresmoi Summary Translation Test Data 1.1
Dušek, Ondřej; Hajič, Jan; Hlaváčová, Jaroslava. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2014
BASE
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16
Jörg Tiedemann: Bitext alignment
In: Machine translation. - Dordrecht [u.a.] : Springer Science + Business Media 27 (2013) 1, 77-79
OLC Linguistik
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17
MTMonkey: A Scalable Infrastructure for a Machine Translation Web Service
In: The Prague bulletin of mathematical linguistics. - Praha : Univ. (2013) 100, 31-40
OLC Linguistik
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18
Khresmoi Query Translation Test Data 1.0
Pecina, Pavel; Dušek, Ondřej; Hajič, Jan. - : Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL), 2013
BASE
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19
A corpus-based finite-state morphological toolkit for contemporary arabic
Attia, Mohammed; Pecina, Pavel; Toral, Antonio. - : Oxford University Press, 2013
BASE
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20
Khresmoi – multilingual semantic search of medical text and images
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