Vine Parsing and Minimum Risk Reranking for Speed and Precision
Vine Parsing and Minimum Risk Reranking for Speed and Precision
复制标题
Vine 解析和最小风险重排序以提高速度和精度
DOI：
--
复制
发表时间：
2006
期刊：
影响因子：
--
通讯作者：
Noah A. Smith
中科院分区：
文献类型：
--
作者：
Markus Dreyer;David A. Smith;Noah A. Smith
我们将描述我们在CoNLL-X共享任务中的条目。该系统包括三个阶段：产生无标记依存关系树的概率Vine解析器(Eisner和N.Smith，2005)、概率关系标记模型和歧视性最小风险重排器(D.Smith和Eisner，2006)。该系统具有训练速度快、译码精度高等特点。我们描述了跨语言错误的来源和改善它们的方法。然后，我们提供了对德语(大量训练数据)和阿拉伯语(很少训练数据)句子的句法分析的详细错误分析。
We describe our entry in the CoNLL-X shared task. The system consists of three phases: a probabilistic vine parser (Eisner and N. Smith, 2005) that produces unlabeled dependency trees, a probabilistic relation-labeling model, and a discriminative minimum risk reranker (D. Smith and Eisner, 2006). The system is designed for fast training and decoding and for high precision. We describe sources of cross-lingual error and ways to ameliorate them. We then provide a detailed error analysis of parses produced for sentences in German (much training data) and Arabic (little training data).