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Human-centred design on crowdsourcing annotation towards improving active learning model performance

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成果类型:
期刊论文
作者:
Dong, Jing;Kang, Yangyang;Liu, Jiawei;Sun, Changlong;Fan, Shu;...
通讯作者:
Wu, D;Liu, XZ
作者机构:
[Dong, Jing] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.
[Kang, Yangyang; Sun, Changlong] Alibaba Grp, Shanghai, Peoples R China.
[Liu, Jiawei; Wu, Dan] Wuhan Univ, Sch Informat Management, Wuhan, Peoples R China.
[Fan, Shu] Sichuan Univ, Sch Publ Adm, Sichuan, Peoples R China.
[Jin, Huchong] Indiana Univ, Luddy Sch Informat Comp & Engn, Bloomington, IN USA.
通讯机构:
[Wu, D ] W
[Liu, XZ ] I
Wuhan Univ, Sch Informat Management, 299 Bayi Rd, Wuhan 430072, Hubei, Peoples R China.
Indiana Univ Bloomington, 107 S Indiana Ave, Bloomington, IN 47405 USA.
语种:
英文
关键词:
Active learning;annotation cost;crowdsourcing;ground truth labels;human annotations;human-centred design
期刊:
Journal of Information Science
ISSN:
0165-5515
年:
2023
机构署名:
本校为第一机构
院系归属:
信息管理学院
摘要:
Active learning in machine learning is an effective approach to reducing the cost of human efforts for generating labels. The iterative process of active learning involves a human annotation step, during which crowdsourcing could be leveraged. It is essential for organisations adopting the active learning method to obtain a high model performance. This study aims to identify effective crowdsourcing interaction designs to promote the quality of human annotations and therefore the natural language processing (NLP)-based machine learning model performance. Specifically, the study experimented wit...

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