Identification of Parallel Sentences in Comparable Monolingual Corpora from Different Registers

Rémi Cardon, Natalia Grabar


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 (specialized 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 classifiers. 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.
Anthology ID:
W18-5610
Volume:
Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis
Month:
October
Year:
2018
Address:
Brussels, Belgium
Venues:
EMNLP | Louhi | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
83–93
Language:
URL:
https://aclanthology.org/W18-5610
DOI:
10.18653/v1/W18-5610
Bibkey:
Cite (ACL):
Rémi Cardon and Natalia Grabar. 2018. Identification of Parallel Sentences in Comparable Monolingual Corpora from Different Registers. In Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis, pages 83–93, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Identification of Parallel Sentences in Comparable Monolingual Corpora from Different Registers (Cardon & Grabar, 2018)
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PDF:
https://aclanthology.org/W18-5610.pdf