Adaptation of machine translation for multilingual information retrieval in medical domain

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F14%3A10289237" target="_blank" >RIV/00216208:11320/14:10289237 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.artmed.2014.01.004" target="_blank" >http://dx.doi.org/10.1016/j.artmed.2014.01.004</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.artmed.2014.01.004" target="_blank" >10.1016/j.artmed.2014.01.004</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Adaptation of machine translation for multilingual information retrieval in medical domain

Popis výsledku v původním jazyce

In this work, we investigate machine translation (MT) of search queries in the context of cross-lingual information retrieval (IR) in the domain of medicine. The main focus is on MT adaptation techniques to increase translation quality, however we also explore MT adaptation to improve cross-lingual IR directly. The experiments described herein have been performed and thoroughly evaluated for MT quality on the datasets created within the Khresmoi project and for IR performance on the CLEF eHealth 2013 datasets on three language pairs: Czech-English, German-English, and French-English. The search query translation results achieved in our experiments are outstanding - our systems outperformed not only our strong baselines, but also the Google Translate and Microsoft Bing Translator in direct comparison carried out on all the language pairs. In terms of the retrieval performance on this particular test collection, a significant improvement over the baseline has been achieved only for Frenc

Název v anglickém jazyce

Adaptation of machine translation for multilingual information retrieval in medical domain

Popis výsledku anglicky

In this work, we investigate machine translation (MT) of search queries in the context of cross-lingual information retrieval (IR) in the domain of medicine. The main focus is on MT adaptation techniques to increase translation quality, however we also explore MT adaptation to improve cross-lingual IR directly. The experiments described herein have been performed and thoroughly evaluated for MT quality on the datasets created within the Khresmoi project and for IR performance on the CLEF eHealth 2013 datasets on three language pairs: Czech-English, German-English, and French-English. The search query translation results achieved in our experiments are outstanding - our systems outperformed not only our strong baselines, but also the Google Translate and Microsoft Bing Translator in direct comparison carried out on all the language pairs. In terms of the retrieval performance on this particular test collection, a significant improvement over the baseline has been achieved only for Frenc

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

AI - Jazykověda

OECD FORD obor

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Projekt

<a href="/cs/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Centrum pro multi-modální interpretaci dat velkého rozsahu</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Artificial Intelligence in Medicine

ISSN

0933-3657

e-ISSN

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Svazek periodika

61

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

21

Strana od-do

165-185

Kód UT WoS článku

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EID výsledku v databázi Scopus

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