A Multilingual Procedure for Dictionary-Based Sentence Alignment
AMTA '98 Proceedings of the Third Conference of the Association for Machine Translation in the Americas on Machine Translation and the Information Soup
Computational Linguistics - Special issue on using large corpora: I
Methods and practical issues in evaluating alignment techniques
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
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
Aligning sentences in bilingual corpora using lexical information
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
On some optimization heuristics for lesk-like WSD algorithms
NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
MRTECEEL '09 Proceedings of the Workshop on Multilingual Resources, Technologies and Evaluation for Central and Eastern European Languages
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The paper presents a bilingual English-Spanish parallel corpus aligned at the paragraph level. The corpus consists of twelve large novels found in Internet and converted into text format with manual correction of formatting problems and errors. We used a dictionary-based algorithm for automatic alignment of the corpus. Evaluation of the results of alignment is given. There are very few available resources as far as parallel fiction texts are concerned, while they are non-trivial case of alignment of a considerable size. Usually, approaches for automatic alignment that are based on linguistic data are applied for texts in the restricted areas, like laws, manuals, etc. It is not obvious that these methods are applicable for fiction texts because these texts have much more cases of non-literal translation than the texts in the restricted areas. We show that the results of alignment for fiction texts using dictionary based method are good, namely, produce state of art precision value.