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Identification of Parallel Sentences in Comparable Monolingual Corpora from Different Registers

Abstract : Parallel aligned sentences provide useful information for different NLP applications. Yet, this kind of data is seldom available, especially for languages other than English. We propose to exploit comparable corpora in French which are distinguished by their registers (spe-cialized and simplified versions) to detect and align parallel sentences. These corpora are related to the biomedical area. Our purpose is to state whether a given pair of specialized and simplified sentences is to be aligned or not. Manually created reference data show 0.76 inter-annotator agreement. We exploit a set of features and several automatic classi-fiers. The automatic alignment reaches up to 0.93 Precision, Recall and F-measure. In order to better evaluate the method, it is applied to data in English from the SemEval STS competitions. The same features and models are applied in monolingual and cross-lingual contexts , in which they show up to 0.90 and 0.73 F-measure, respectively.
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Contributor : Natalia Grabar <>
Submitted on : Wednesday, January 2, 2019 - 3:40:09 PM
Last modification on : Tuesday, November 3, 2020 - 9:54:04 AM
Long-term archiving on: : Wednesday, April 3, 2019 - 3:49:00 PM


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  • HAL Id : halshs-01968351, version 1



Rémi Cardon, Natalia Grabar. Identification of Parallel Sentences in Comparable Monolingual Corpora from Different Registers. LOUHI 2018:The Ninth International Workshop on Health Text Mining and Information Analysis, Oct 2018, Bruxelles, Belgium. ⟨halshs-01968351⟩



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