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Evaluating and Combining Name Entity Recognition Systems 评价和组合名称实体识别系统
Pub Date : 1900-01-01 DOI: 10.18653/v1/W16-2703
Ridong Jiang, Rafael E. Banchs, Haizhou Li
Name entity recognition (NER) is an important subtask in natural language processing. Various NER systems have been developed in the last decade. They may target for different domains, employ different methodologies, work on different languages, detect different types of entities, and support different inputs and output formats. These conditions make it difficult for a user to select the right NER tools for a specific task. Motivated by the need of NER tools in our research work, we select several publicly available and well-established NER tools to validate their outputs against both Wikipedia gold standard corpus and a small set of manually annotated documents. All the evaluations show consistent results on the selected tools. Finally, we constructed a hybrid NER tool by combining the best performing tools for the domains of our interest.
名称实体识别是自然语言处理中的一个重要子任务。在过去的十年中,各种NER系统得到了发展。它们可能针对不同的领域,采用不同的方法,使用不同的语言,检测不同类型的实体,并支持不同的输入和输出格式。这些条件使得用户难以为特定任务选择正确的NER工具。由于研究工作中需要NER工具,我们选择了几个公开可用且完善的NER工具来验证它们的输出,对照维基百科金标准语料库和一小组手动注释的文档。所有的评估都显示了所选工具的一致结果。最后,我们结合了我们感兴趣的领域中性能最好的工具,构建了一个混合NER工具。
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引用次数: 74
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NEWS@ACM
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