德语统计标记器和基于规则的标记器的比较
Comparing a statistical and a rule-based tagger for German
摘要：
在本文中，我们提出了比较的结果，德国的统计标记的基础上决策树和基于规则的Brill-Tagger德国。我们使用相同的训练语料库（因此使用相同的标记集）来训练两个标记器。然后，我们将标记器应用于相同的测试语料库，并比较它们各自的行为，特别是它们的出错率。这两个标签执行类似的出错率约为5%。从详细的错误分析可以看出，基于规则的标注器比统计标注器在未登录词方面存在更多的问题。但是对于多方面模糊的标记，结果是相反的。如果在外部词典（例如Gertwol系统）的帮助下将未知词馈送到标记器中，则基于规则的标记器的出错率下降到4.7%，并且统计标记器的相应比率下降到约3.7%。通过使用一个标记器的输出来帮助另一个标记器来组合标记器并没有导致任何进一步的改进。
英文摘要：
In this paper we present the results of comparing a statistical tagger for German based on decision trees and a rule-based Brill-Tagger for German. We used the same training corpus (and therefore the same tag-set) to train both taggers. We then applied the taggers to the same test corpus and compared their respective behavior and in particular their error rates. Both taggers perform similarly with an error rate of around 5%. From the detailed error analysis it can be seen that the rule-based tagger has more problems with unknown words than the statistical tagger. But the results are opposite for tokens that are many-ways ambiguous. If the unknown words are fed into the taggers with the help of an external lexicon (such as the Gertwol system) the error rate of the rule-based tagger drops to 4.7%, and the respective rate of the statistical taggers drops to around 3.7%. Combining the taggers by using the output of one tagger to help the other did not lead to any further improvement.
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。
