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The recognition of Laos organization name based on a cascaded conditional random fields

机译:基于级联条件随机字段的老挝组织名称识别

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摘要

The recognition of Laos organization name is a difficult problem in the entity recognition of Laos language. This paper presents a algorithm of Laos organization name recognition model based on cascaded conditional random fields. The algorithm solved the recognition of easy entity such as person name and location name in the lower model of conditional random fields(CRFs) and served the recognition of complicated organization names on the higher CRFs. This paper designed a efficient feature template and automatic feature selection algorithm for the conditional random fields model of organization names. In the open test of a mass linguistic data, the recall rate reached 79.67%, precision rate reached 77.72%, F - measure reached 78.68%.
机译:老挝组织名称的识别是老挝语言实体识别中的一个难题。本文提出了一种基于级联条件随机场的老挝组织名称识别模型算法。该算法解决了在条件随机字段(CRF)的下层模型中识别诸如人名和位置名之类的简单实体的问题,并在较高的CRF上用于识别复杂的组织名称。本文针对组织名称的条件随机域模型设计了一种高效的特征模板和自动特征选择算法。在大众语言数据的公开测试中,召回率达到79.67%,准确率达到77.72%,F-测度达到78.68%。

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