Disambiguating named entitieswith deep supervised learning via crowd labels

Le-kui ZHOU, Si-liang TANG, Jun XIAO, Fei WU, Yue-ting ZHUANG

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Front. Inform. Technol. Electron. Eng ›› 2017, Vol. 18 ›› Issue (1) : 97-106. DOI: 10.1631/FITEE.1601835
Article

Disambiguating named entitieswith deep supervised learning via crowd labels

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Abstract

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.

Keywords

Named entity disambiguation / Crowdsourcing / Deep learning

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Le-kui ZHOU, Si-liang TANG, Jun XIAO, Fei WU, Yue-ting ZHUANG. Disambiguating named entitieswith deep supervised learning via crowd labels. Front. Inform. Technol. Electron. Eng, 2017, 18(1): 97‒106 https://doi.org/10.1631/FITEE.1601835

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2017 Zhejiang University and Springer-Verlag Berlin Heidelberg
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