CLC number: TP391.4
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2017-01-05
Cited: 2
Clicked: 8223
Le-kui Zhou, Si-liang Tang, Jun Xiao, Fei Wu, Yue-ting Zhuang. Disambiguating named entities with deep supervised learning via crowd labels[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 97-106.
@article{title="Disambiguating named entities with deep supervised learning via crowd labels",
author="Le-kui Zhou, Si-liang Tang, Jun Xiao, Fei Wu, Yue-ting Zhuang",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="18",
number="1",
pages="97-106",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601835"
}
%0 Journal Article
%T Disambiguating named entities with deep supervised learning via crowd labels
%A Le-kui Zhou
%A Si-liang Tang
%A Jun Xiao
%A Fei Wu
%A Yue-ting Zhuang
%J Frontiers of Information Technology & Electronic Engineering
%V 18
%N 1
%P 97-106
%@ 2095-9184
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601835
TY - JOUR
T1 - Disambiguating named entities with deep supervised learning via crowd labels
A1 - Le-kui Zhou
A1 - Si-liang Tang
A1 - Jun Xiao
A1 - Fei Wu
A1 - Yue-ting Zhuang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 1
SP - 97
EP - 106
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1601835
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.
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