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Conditional Random Field（CRF）是重要的串学习模型，广泛用于自然语言处理的各个领域。CRF++是CRF的一个高效的实现，具有可扩展性好，功能强大的优点。-Conditional Random Field (CRF) is an important learning model series , widely used in natural language processing in various fields. CRF CRF is the realization
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Conditional Random Field（CRF）是重要的串学习模型，广泛用于自然语言处理的各个领域。CRF++是CRF的一个高效的实现，具有可扩展性好，功能强大的优点。-Conditional Random Field (CRF) is an important learning model series , widely used in natural language processing in various fields. CRF CRF is the realization
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Hieu Xuan Phan & Minh Le Nguyen 利用CRF统计模型写的可用于英文命名实体识别、英文分词的工具（开放源码）。CRF模型最早由Lafferty提出，全名conditional random fields，该模型后来被广泛地应用在语言和图像处理领域，并随之出现了很多的变体。FlexCRF就是对CRF模型的一个实现应用工具，可用于文本信息处理
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基于CRF（conditional random fields)统计模型的文本人名识别工具源代码，是Mallet开放源码项目的一部分-based on CRF (conditional random fields) statistical model of text my name recognition tools source code, open source Mallet is part of the project
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CRFsuite is a very fast implmentation of the Conditional Random Fields (CRF) algorithm. It handles tens of thousands sentences in merely one second.
In comparison to CRF++, CRFSuite yields substantially better efficiency performance
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crf++-0.53.zip CRF++ is a simple, customizable, and open source implementation of Conditional Random Fields (CRFs) for segmenting/labeling sequential data. CRF++ is designed for generic purpose and will be applied to a variety of NLP tasks, such as N
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说明：
基于条件随机场模型的经典理论介绍，广泛应用于命名实体识别，实体关系识别领域。-Note: Based on Conditional Random Fields model describes the classical theory is widely used in named entity recognition, entity-relationship identification field.
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说明：基于条件随机场模型的实体关系识别，内含条件随机场的理论介绍以及该理论应用于实体关系识别的实验及实验分析
-Note: Based on Conditional Random Fields model entity-relationship recognition, implied condition of the theoretical descr iption with the airport, as well as the theory is applied to entity-r
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CRF最权威介绍资料，介绍了CRF的来龙去脉-Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
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CRF，是一种判别式图模型，因为其强大的表达能力和出色的性能，得到了广泛的应用-Conditional random fields
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