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
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.
