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ACL
1998

Improving Statistical Natural Language Translation with Categories and Rules

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Improving Statistical Natural Language Translation with Categories and Rules
This paper describes an all level approach on statistical natural language translation (SNLT). Without any predefined knowledge the system learns a statistical translation lexicon (STL), word classes (WCs) and translation rules (TRs) from a parallel corpus thereby producing a generalized form of a word alignment (WA). The translation process itself is realized as a beam search. In our method example-based techniques enter an overall statistical approach leading to about 50 percent correctly translated sentences applied to the very difficult EnglishGerman VERBMOBIL spontaneous speech corpus.
Franz Josef Och, Hans Weber
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 1998
Where ACL
Authors Franz Josef Och, Hans Weber
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