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...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。展开更多
Named entity disambiguation （NED） is the task of linking mentions of ambiguous entities to their referenced entities in a knowledge base such as Wikipedia. We propose an approach to effectively disentangle the discr...Named entity disambiguation （NED） is the task of linking mentions of ambiguous entities to their referenced entities in a knowledge base such as Wikipedia. We propose an approach to effectively disentangle the discriminative features in the manner of collaborative utilization of collective wisdom （via human-labeled crowd labels） and deep learning （via human-generated data） for the NED task. In particular, we devise a crowd model to elicit the underlying features （crowd features） from crowd labels that indicate a matching candidate for each mention, and then use the crowd features to fine-tune a dynamic convolutional neural network （DCNN）. The learned DCNN is employed to obtain deep crowd features to enhance traditional hand-crafted features for the NED task. The proposed method substantially benefits from the utilization of crowd knowledge （via crowd labels） into a generic deep learning for the NED task. Experimental analysis demonstrates that the proposed approach is superior to the traditional hand-crafted features when enough crowd labels are gathered.展开更多
文摘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。
基金supported by the National Basic Research Program of China(No.2015CB352300)the National Natural Science Foundation of China(Nos.61402401 and U1509206)+3 种基金the Zhejiang Provincial Natural Science Foundation of China(No.LQ14F010004)the China Knowledge Centre for Engineering Sciences and Technologythe Fundamental Research Funds for the Central Universitiesthe Qianjiang Talents Program of Zhejiang Province,China
文摘Named entity disambiguation （NED） is the task of linking mentions of ambiguous entities to their referenced entities in a knowledge base such as Wikipedia. We propose an approach to effectively disentangle the discriminative features in the manner of collaborative utilization of collective wisdom （via human-labeled crowd labels） and deep learning （via human-generated data） for the NED task. In particular, we devise a crowd model to elicit the underlying features （crowd features） from crowd labels that indicate a matching candidate for each mention, and then use the crowd features to fine-tune a dynamic convolutional neural network （DCNN）. The learned DCNN is employed to obtain deep crowd features to enhance traditional hand-crafted features for the NED task. The proposed method substantially benefits from the utilization of crowd knowledge （via crowd labels） into a generic deep learning for the NED task. Experimental analysis demonstrates that the proposed approach is superior to the traditional hand-crafted features when enough crowd labels are gathered.
