Learning Subsequential Transducers for Pattern Recognition Interpretation Tasks
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Data Driven Approach Applied to the OSTIA Algorithm
ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Analogical Translation of Medical Words in Different Languages
GoTAL '08 Proceedings of the 6th international conference on Advances in Natural Language Processing
Translating medical terminologies through word alignment in parallel text corpora
Journal of Biomedical Informatics
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This paper presents a method to automatically translate a large class of terms in the biomedical domain from one language to another; it is evaluated on translations between French and English. It relies on a machine-learning technique that infers transducers from examples of bilingual word pairs; no additional resource or knowledge is needed. Then, these transducers, making the most of the high regularity of translation discovered in the examples, can be used to translate unseen French terms into English or vice versa. We report evaluations that show that this technique achieves high precision, reaching up to 85% of correct translations for both French to English and English to French tasks.