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On the Automatic Learning of Bilingual Resources: Some Relevant Factors for Machine Translation
SBIA '08 Proceedings of the 19th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
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CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
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SBIA'10 Proceedings of the 20th Brazilian conference on Advances in artificial intelligence
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Speeding up target-language driven part-of-speech tagger training for machine translation
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FinTAL'06 Proceedings of the 5th international conference on Advances in Natural Language Processing
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PROPOR'10 Proceedings of the 9th international conference on Computational Processing of the Portuguese Language
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PROPOR'12 Proceedings of the 10th international conference on Computational Processing of the Portuguese Language
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This paper describes the current status of development of an open-source shallow-transfer machine translation (MT) system for the [European] Portuguese $\leftrightarrow$ Spanish language pair, developed using the OpenTrad Apertium MT toolbox (www.apertium.org). Apertium uses finite-state transducers for lexical processing, hidden Markov models for part-of-speech tagging, and finite-state-based chunking for structural transfer, and is based on a simple rationale: to produce fast, reasonably intelligible and easily correctable translations between related languages, it suffices to use a MT strategy which uses shallow parsing techniques to refine word-for-word MT. This paper briefly describes the MT engine, the formats it uses for linguistic data, and the compilers that convert these data into an efficient format used by the engine, and then goes on to describe in more detail the pilot Portuguese$\leftrightarrow$Spanish linguistic data.