Inducing History Representations for Broad Coverage Statistical Parsing

Inducing History Representations for Broad Coverage Statistical Parsing
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DOI：
10.3115/1073445.1073459
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发表时间：
2003-05
期刊：
Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - NAACL '03
影响因子：
--
通讯作者：
James Henderson
James Henderson
中科院分区：
其他
文献类型：
--
作者：
James Henderson

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我们提出了一种神经网络方法诱导解析历史的表示，并使用这些历史表示来估计统计左角解析器所需的概率。由此产生的统计分析器实现性能（89.1%F-措施）的Penn Treebank，这是只有0.6%低于目前最好的解析器，尽管使用较小的词汇量和较少的语言知识。这一成功的关键是使用结构确定的软偏见，诱导的解析历史的表示，没有使用硬独立的假设。
We present a neural network method for inducing representations of parse histories and using these history representations to estimate the probabilities needed by a statistical left-corner parser. The resulting statistical parser achieves performance (89.1% F-measure) on the Penn Treebank which is only 0.6% below the best current parser for this task, despite using a smaller vocabulary size and less prior linguistic knowledge. Crucial to this success is the use of structurally determined soft biases in inducing the representation of the parse history, and no use of hard independence assumptions.