Probabilistic Disambiguation Models for Wide-Coverage HPSG Parsing
Probabilistic Disambiguation Models for Wide-Coverage HPSG Parsing
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DOI：
10.3115/1219840.1219851
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发表时间：
2005-06
期刊：
影响因子：
--
通讯作者：
Yusuke Miyao;Junichi Tsujii
中科院分区：
文献类型：
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作者：
Yusuke Miyao;Junichi Tsujii
本文报道了对数线性模型的发展，广泛覆盖的HPSG句法分析中的消歧。对数线性模型的估计需要很高的计算成本，特别是对于覆盖范围很广的文法。使用技术来降低估计成本，我们使用Penn Tree-bank的20个部分来训练模型。一系列的实验经验评估的估计技术，也检查了在现实世界的句子解析的消歧模型的性能。
This paper reports the development of log-linear models for the disambiguation in wide-coverage HPSG parsing. The estimation of log-linear models requires high computational cost, especially with wide-coverage grammars. Using techniques to reduce the estimation cost, we trained the models using 20 sections of Penn Tree-bank. A series of experiments empirically evaluated the estimation techniques, and also examined the performance of the disambiguation models on the parsing of real-world sentences.