Abstract
Keyphrase extraction is a very important task in text mining. However, keyphrase extraction of news articles cannot be addressed by existing machine-based approaches effectively because of various reasons. This paper employs crowdsourcing for keyphrase extraction of news articles. We first design a proper crowdsourcing mechanism to extract keyphrases from news articles and then adapt three truth inference algorithms (namely IMLK, IMLK-I, and IMLK-ED) for integrating multiple lists of keyphrases provided by workers. The experiments show that crowdsourcing can significantly improve the performance of the machine-based approach (i.e., KeyRank).
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Acknowledgment
This work is partially supported by the US National Science Foundation under grant IIS-1115417, the National Natural Science Foundation of China under Grant (61725205, 61876217, 3177167, 31671589, and 31371533), the Key Laboratory of Agricultural Electronic Commerce, Ministry of Agriculture of China under Grant (AEC2018003), the Anhui Foundation for Science and Technology Major Project under Grant (16030701092 and 18030901034), the 2016 Anhui Foundation for Natural Science Major Project of the Higher Education Institutions under grant (kJ2016A836), and the Hefei Major R&D Projects of Key Technologies under grant (J2018G14).
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Wang, Q., Zhong, J., Gu, L., Yang, K., Sheng, V.S. (2020). Extracting Keyphrases from News Articles Using Crowdsourcing. In: Yuan, X., Elhoseny, M. (eds) Urban Intelligence and Applications. Studies in Distributed Intelligence . Springer, Cham. https://doi.org/10.1007/978-3-030-45099-1_17
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DOI: https://doi.org/10.1007/978-3-030-45099-1_17
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