Bilingual Sentence Alignment: Balancing Robustness and Accuracy
Machine Translation
Computational Linguistics - Special issue on using large corpora: I
Bitext maps and alignment via pattern recognition
Computational Linguistics
Aligning sentences in parallel corpora
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
A program for aligning sentences in bilingual corpora
ACL '91 Proceedings of the 29th annual meeting on Association for Computational Linguistics
Char_align: a program for aligning parallel texts at the character level
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Aligning a parallel English-Chinese corpus statistically with lexical criteria
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Aligning portuguese and chinese parallel texts using confidence bands
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Extracting Equivalents from Aligned Parallel Texts: Comparison of Measures of Similarity
IBERAMIA-SBIA '00 Proceedings of the International Joint Conference, 7th Ibero-American Conference on AI: Advances in Artificial Intelligence
A Self-Learning Method of Parallel Texts Alignment
AMTA '00 Proceedings of the 4th Conference of the Association for Machine Translation in the Americas on Envisioning Machine Translation in the Information Future
Knowledge intensive word alignment with KNOWA
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Longest sorted sequence algorithm for parallel text alignment
EUROCAST'05 Proceedings of the 10th international conference on Computer Aided Systems Theory
Using natural alignment to extract translation equivalents
PROPOR'06 Proceedings of the 7th international conference on Computational Processing of the Portuguese Language
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This paper describes a language independent method for alignment of parallel texts that makes use of homograph tokens for each pair of languages. In order to filter out tokens that may cause misalignment, we use confidence bands of linear regression lines instead of heuristics which are not theoretically supported. This method was originally inspired on work done by Pascale Fung and Kathleen McKeown, and Melamed, providing the statistical support those authors could not claim.