论文标题
结构化的加权违规行为mira
The Structured Weighted Violations MIRA
论文作者
论文摘要
我们提出了结构化的加权违规行为MIRA（SWVM），这是一种基于Mira（Crammer and Singer，2003）和结构化加权违规（SWVP）（Dror和Reichart，2016）的新结构化预测算法。我们证明（Dror和Reichart，2016）与强大的结构化预测算法相结合可以提高序列标签任务的性能。在使用句法块和命名实体识别（NER）的实验中，新算法显然优于原始MIRA以及原始的结构化感知器和SWVP。我们的代码可在https://github.com/dorringel/swvm上找到。
We present the Structured Weighted Violation MIRA (SWVM), a new structured prediction algorithm that is based on an hybridization between MIRA (Crammer and Singer, 2003) and the structured weighted violations perceptron (SWVP) (Dror and Reichart, 2016). We demonstrate that the concepts developed in (Dror and Reichart, 2016) combined with a powerful structured prediction algorithm can improve performance on sequence labeling tasks. In experiments with syntactic chunking and named entity recognition (NER), the new algorithm substantially outperforms the original MIRA as well as the original structured perceptron and SWVP. Our code is available at https://github.com/dorringel/SWVM.
