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Extracting Keyphrases from News Articles Using Crowdsourcing

Wang, Qingren / Zhong, Jinqin / Gu, Lichuan / Yang, Kai / Sheng, Victor S.

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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Extracting Keyphrases from News Articles Using Crowdsourcing

Wang, Qingren / Zhong, Jinqin / Gu, Lichuan / Yang, Kai / Sheng, Victor S.

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).

Extracting Keyphrases from News Articles Using Crowdsourcing

Wang, Qingren / Zhong, Jinqin / Gu, Lichuan / Yang, Kai / Sheng, Victor S.

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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Title:

Extracting Keyphrases from News Articles Using Crowdsourcing

Additional title:

Stud. in Distributed Intelligence

Contributors:

Yuan, Xiaohui (editor) / Elhoseny, Mohamed (editor) / Wang, Qingren (author) / Zhong, Jinqin (author) / Gu, Lichuan (author) / Yang, Kai (author) / Sheng, Victor S. (author)

Published in:

Urban Intelligence and Applications ; Chapter: 17 ; 217-230

Studies in Distributed Intelligence

Publication date:

2020-06-26

Size:

14 pages

ISBN:

978-3-030-45099-1 , 978-3-030-45098-4

ISSN:

2662-3714 , 2662-3706

DOI:

https://doi.org/10.1007/978-3-030-45099-1_17

Type of media:

Article/Chapter (Book)

Type of material:

Electronic Resource

Language:

English

Keywords:

Keyphrase extraction , Crowdsourcing , Ground truth inference , Grade calculation Computer Science , Machine Learning , Urban Geography / Urbanism (inc. megacities, cities, towns) , IT in Business , Complex Systems

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