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Syntactic and Semantic Impact of Prepositions in Machine Translation : An Empirical Study of French-English Translation of Prepositions ‘à’, ‘de’ and ‘en’

Violaine Prince 1 
1 TEXTE - Exploration et exploitation de données textuelles
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : This paper presents a study about ambiguous French prepositions, stressing out their roles as dependencies introducers, in order to derive some translation heuristics into English, based on a FrenchEnglish set of parallel texts. These heuristics are formulated out of statistical observations and use some up-to-date results in Machine Translation (MT). Their originality mostly relies upon two items: (1) The importance given to syntax and dependency relations, along with lexicons, the latter being well browsed by the present literature in the domain (2) The existence of intrinsic semantics in prepositions, something rather discarded in NLP literature devoted to statistical MT, that tends to point at the most appropriate translation. An experiment has been run on corpora in both languages, using a dependency parser in the source language, and results looked to be encouraging for a “step by step approach” for MT improvement.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-03091307
Contributor : Violaine Prince-Barbier Connect in order to contact the contributor
Submitted on : Wednesday, December 30, 2020 - 8:15:39 PM
Last modification on : Friday, August 5, 2022 - 3:03:22 PM

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Violaine Prince. Syntactic and Semantic Impact of Prepositions in Machine Translation : An Empirical Study of French-English Translation of Prepositions ‘à’, ‘de’ and ‘en’. Vetulani Z., Paroubek P., Kubis M. (eds) Human Language Technology. Challenges for Computer Science and Linguistics. LTC 2017. Lecture Notes in Computer Science, vol 12598., 12598, pp.273-287, In press, Lecture Notes in Computer Science, 978-3-030-66526-5. ⟨10.1007/978-3-030-66527-2_20⟩. ⟨lirmm-03091307⟩

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