复合分裂的经验方法
Empirical Methods for Compound Splitting
摘要：
复合词对于诸如机器翻译（MT）之类的NLP应用是一个挑战。我们介绍了从单语和平行语料库中学习分裂规则的方法。我们评估他们对黄金标准，并衡量其对统计MT系统的性能的影响。结果显示，在德语-英语名词短语翻译任务中，MT的准确率为99.1%，性能增益为0.039 BLEU。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
