Empirical Methods for Compound Splitting
<|reference_start|>Empirical Methods for Compound Splitting: 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.<|reference_end|>
@article{koehn2003empirical,
    title={Empirical Methods for Compound Splitting},
    author={Philipp Koehn and Kevin Knight},
    journal={arXiv preprint arXiv:cs/0302032},
    year={2003},
    archivePrefix={arXiv},
    eprint={cs/0302032},
    primaryClass={cs.CL}
}