Fast and Accurate Arc Filtering for Dependency Parsing
We propose a series of learned arc filters to speed up graph-based dependency parsing. A cascade of filters identify implausible head-modifier pairs, with time complexity that is first linear, and then quadratic in the length of the sentence. The linear filters reliably predict, in context, words that are roots or leaves of dependency trees, and words that are likely to have heads on their left or right. We use this information to quickly prune arcs from the dependency graph. More than 78％ of total arcs are pruned while retaining 99.5％of the true dependencies. These filters improve the speed of two state-ofthe- art dependency parsers, with low overhead and negligible loss in accuracy.
Shane Bergsma Colin Cherry
Department of Computing ScienceUniversity of Alberta Institute for Information Technology National Research Council Canada
国际会议
The 23rd International Conference on Computational Linguistics(第23届国际计算语言学大会)
北京
英文
53-61
2010-08-01（万方平台首次上网日期，不代表论文的发表时间）