Translating collocations for bilingual lexicons: a statistical approach
Computational Linguistics
Foundations of statistical natural language processing
Foundations of statistical natural language processing
A program for aligning sentences in bilingual corpora
Computational Linguistics - Special issue on using large corpora: I
Computational Linguistics - Special issue on using large corpora: I
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
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
K-vec: a new approach for aligning parallel texts
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
HMM-based word alignment in statistical translation
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Combining clues for word alignment
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
A probability model to improve word alignment
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Improved statistical alignment models
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Extensions to HMM-based statistical word alignment models
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
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We are presenting a new, hybrid alignment architecture for aligning bilingual, linguistically annotated parallel corpora. It is able to align simultaneously at paragraph, sentence, phrase and word level, using statistical and heuristic cues, along with linguistics-based rules. The system currently aligns English and German texts, and the linguistic annotation used covers POS-tags, lemmas and syntactic constitutents. However, as the system is highly modular, we can easily adapt it to new language pairs and other types of annotation. The hybrid nature of the system allows experiments with a variety of alignment cues to find solutions to word alignment problems like the correct alignment of rare words and multiwords, or how to align despite syntactic differences between two languages. First performance tests are promising, and we are setting up a gold standard for a thorough evaluation of the system.