摘要命名实体识别是自然语言处理的基础性任务，其结果具有广泛的应用。关联数据由于具有丰富的语义知识，能够对现有命名实体识别进一步完善。本文实现了一个基于关联数据的可配置的中英文命名实体识别系统，在识别过程中对实体进行消歧并对识别结果进行扩展，为命名实体识别的进一步完善提供了新的思路。具体包括：基于DBpedia构造了跨领域的中英文命名实体词典；设计了一个基于Hive的分布式管理数据存储模型，基于该模型实现了对DBpedia数据集的组织、存储以及扩展；设计了一个基于图的命名实体识别算法，该算法能够充分利用关联数据的语义关系对命名实体进行消歧，并且基于DBpedia Spotlight NER Corpus对算法进行测试，并将算法结果与DBpedia Spotlight、NERSO以及Zwmanta三个系统进行对比评价，结果表明本文实现的算法在查全率、查准率、F值上具有更好的表现。
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