Evaluative prefixes in translation: From automatic alignment to semantic categorization
DOI:
https://doi.org/10.33011/lilt.v11i.1371Keywords:
contrastive morphology, evaluative prefixation, semantics, translation, parallel corpora, Natural Language Processing, automatic alignment, French, EnglishAbstract
This article aims to assess to what extent translation can shed light on the semantics of French evaluative prefixation by adopting Noël (2003)’s ‘translations as evidence for semantics’ approach. In French, evaluative prefixes can be classified along two dimensions (cf. (Fradin and Montermini 2009)): (1) a quantity dimension along a maximum/minimum axis and the semantic values BIG and SMALL, and (2) a quality dimension along a positive/negative axis and the values GOOD (EXCESS; HIGHER DEGREE) and BAD (LACK; LOWER DEGREE). In order to provide corpus-based insights into this semantic categorization, we analyze French evaluative prefixes alongside their English translation equivalents in a parallel corpus. To do so, we focus on periphrastic translations, as they are likely to ‘spell out’ the meaning of the French prefixes. The data used were extracted from the Europarl parallel corpus (Koehn 2005; Cartoni and Meyer 2012). Using a tailormade program, we first aligned the French prefixed words with the corresponding word(s) in English target sentences, before proceeding to the evaluation of the aligned sequences and the manual analysis of the bilingual data. Results confirm that translation data can be used as evidence for semantics in morphological research and help refine existing semantic descriptions of evaluative prefixes.
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