Brains, not brawn: The use of "smart" comparable corpora in bilingual terminology mining

Current research in text mining favors the quantity of texts over their representativeness. But for bilingual terminologymining, and for many language pairs, large comparable corpora are not available. More importantly, as terms are defined vis-à-vis a specific domain with a restricted register, it is expected that the representativeness rather than the quantity of the corpus matters more in terminology mining. Our hypothesis, therefore, is that the representativeness of the corpus is more important than the quantity and ensures the quality of the acquired terminological resources. This article tests this hypothesis on a French-Japanese bilingual term extraction task. To demonstrate how important the type of discourse is as a characteristic of the comparable corpora, we used a state-of-the-art multilingual terminology mining chain composed of two extraction programs, one in each language, and an alignment program. We evaluated the candidate translations using a reference list, and found that taking discourse type into account resulted in candidate translations of a better quality even when the corpus size was reduced by half.