Parallel corpora segmentation using anchor words

Authors:
Francisco Nevado;Francisco Casacuberta;Enrique Vidal
Affiliations:
Universidad Politécnica de Valencia;Universidad Politécnica de Valencia;Universidad Politécnica de Valencia
Venue:
EAMT '03 Proceedings of the 7th International EAMT workshop on MT and other Language Technology Tools, Improving MT through other Language Technology Tools: Resources and Tools for Building MT
Year:
2003

Citing 5
Cited 3

A statistical approach to machine translation

Computational Linguistics
Inference of Finite-State Transducers by Using Regular Grammars and Morphisms

ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
Finite-State Speech-to-Speech Translation

ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 1 - Volume 1
The mathematics of statistical machine translation: parameter estimation

Computational Linguistics - Special issue on using large corpora: II
Improved statistical alignment models

ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics

Improving IBM word-alignment model 1

ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
A statistical approach to crosslingual natural language tasks

Journal of Algorithms
Using normalized alignment scores to detect incorrectly aligned segments

Proceedings of the 2nd international workshop on Patent information retrieval

Quantified Score

Hi-index	0.00

Visualization

Abstract

A new technique for monotone segmentation of parallel corpora is introduced. This segmentation is based on a set of anchor words which are defined manually. The parallel segments are computed using a dynamic programming algorithm. To assess this technique, finite-state transducers are inferred from both non-segmented and segmented corpora. Experiments have been carried out with Spanish-English and Italian-English translation tasks. This technique has proven useful in improving the results with respect to those obtained with unsegmented corpora.