Compounded words are a challenge for NLP applications such as machine translation (MT). We introduce methods to learn splitting rules from monolingual and parallel corpora. We evaluate them against a gold standard and measure their impact on performance of statistical MT systems. Results show accuracy of 99.1% and performance gains for MT of 0.039 BLEU on a German-English noun phrase translation task.
@article{arxiv.cs/0302032,
title = {Empirical Methods for Compound Splitting},
author = {Philipp Koehn and Kevin Knight},
journal= {arXiv preprint arXiv:cs/0302032},
year = {2007}
}