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Bengali Named Entity Recognition Using Margin Infused Relaxed Algorithm

机译:孟加拉利用利润infused轻松算法命名实体识别

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The present work describes the automatic recognition of named entities based on language independent and dependent features. Margin Infused Relaxed Algorithm is applied for the first time in order to learn named entities for Bengali language. We used openly available annotated corpora with twelve different tagset defined in IJCNLP-08 NERSSEAL shared task and obtained 91.23%, 87.29% and 89.69% precision, recall and F-measure respectively. The proposed work outperforms the existing models with satisfactory margin.
机译:本工作描述了基于语言独立和依赖功能的命名实体自动识别。第一次应用裕度infdeed放松算法,以便学习孟加拉语的命名实体。我们在IJCNLP-08 NERSSEA共享任务中使用了12个不同的标签集,在IJCNLP-08 NERSSEAL共享任务中使用了12个不同的标签,分别获得了91.23%,87.29%和89.69%的精度,召回和F测量。拟议的工作优于现有的模型,以满意的余量。

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