Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
A program for aligning sentences in bilingual corpora
Computational Linguistics - Special issue on using large corpora: I
Bitext correspondences through rich mark-up
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Improving Machine Translation Performance by Exploiting Non-Parallel Corpora
Computational Linguistics
A DOM tree alignment model for mining parallel data from the web
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Mining bilingual data from the web with adaptively learnt patterns
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Improved unsupervised sentence alignment for symmetrical and asymmetrical parallel corpora
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Automatic acquisition of chinese–english parallel corpus from the web
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
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Sentence alignment is an enabling technology which extracts mass of bilingual corpora automatically from the vast and ever-growing Web pages. In this paper, we propose a novel graph-based sentence alignment approach. Compared with the existing approaches, ours is more resistant to the noise and structure diversity of Web pages by considering sentence structural features. We formulate sentence alignment to be a matching problem between nodes (bilingual sentences) of a bipartite graph. The maximum-weighted bipartite graph matching algorithm is first applied to sentence alignment for global optimal matching. Moreover, sentence merging and aligned sentence pattern detection are used to deal with the many-to-many matching issue and the low probability of aligned sentences with few mutual translated words issue respectively. We achieve good precision over 85% and recall over 80% on manually annotated data and 1 million aligned sentence pairs with over 82% accuracy are extracted from 0.8 million bilingual pages.