Learning deep networks with crowdsourcing for relevance evaluation

Learning deep networks with crowdsourcing for relevance evaluation

Abstract In this paper, we propose a novel relevance evaluation method using labels collected from crowdsourcing. The proposed method not only predicts the relevance between query texts and responses in information retrieval systems but also performs the label aggregation tasks simultaneously. It fi...

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Bibliographic Details
Main Authors: Ming Wu, Xiaochun Yin, Qianmu Li, Jing Zhang, Xinqi Feng, Qi Cao, Haiyuan Shen
Format: Article
Language:English
Published: SpringerOpen 2020-04-01
Series:EURASIP Journal on Wireless Communications and Networking
Subjects:
Crowdsourcing
Relevance evaluation
Information retrieval
Deep learning
Online Access:http://link.springer.com/article/10.1186/s13638-020-01697-2

Internet

http://link.springer.com/article/10.1186/s13638-020-01697-2

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