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Reducing parameter space for word alignment

Hervé Dejean, Eric Gaussier, Cyril Goutte, Kenji Yamada
This paper presents experimental results to reduce the parameter space for word alignment algorithm. We use IBM Model 4 as a baseline. We applied a word lemmatizer program and a term extraction algorithm to preprocess a training corpus to reduce the model parameter space. We obtained an improvement in the alignment error rate by the additional components. Available from the http://www.cs.unt.edu/~rada/wpt/NAACL/HLT Workshop. Building and Using Parallel Texts: Data Driven Machine Translation and Beyond website.
http://www.cs.unt.edu/~rada/wpt/NAACL/HLT Workshop Building and Using Parallel Texts: Data Driven Machine Translation and Beyond, Edmonton, Canada, May 31, 2003.
2003
2003/051

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Dejean.pdf (41.78 kB)