Abstract
Buzzword detection technology is of great significance for reflecting the language life situation, describing the actual state of language usage, and developing language resources. At present, the “Top Ten Annual Buzzwords in Chinese Media” released every year are extracted based on a human-computer cooperation method, which consists of buzzword detection technology and selection by human experts. But the existing buzzword detection technology has the following shortcomings: coarse statistical granularity, slow query, large amount of candidate buzzwords and a high proportion of “non-word” character sequences. In view of those shortcomings, this paper proposes a data storage solution using the InfluxDB time series database based on the daily cycle, two buzzword detection models comprising of logistic curve fitting and abnormal rising segment identification, and utilizing the phrase mining technology for quality scoring and filtering of buzzword candidates. The comparison experiments show that the technology proposed in this paper can achieve better results, significantly reduce the subsequent manual workload, provide more statistical information on buzzword candidates and a more efficient data query method.
Supported by the 2020 Key Project of the 13th Five-Year Plan of the State Language Commission (ZDI135-131), the 2019 Key Project of the 13th Five-Year Plan of the State Language Commission (ZDI135-105), the project of Beijing Advanced Innovation Center for Language Resources (TYZ19005), and the Innovation Fund for Chinese and Foreign Graduate Student (20YCX143).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Yang, J.-G.: The linguistic research and scientific confirmation of popular words and phrases. Lang. Teach. Linguist. Stud. 6, 63–70 (2004). (in Chinese)
Hou, M., Yang, E.-H.: Ten years of language monitoring research in China. Appl. Linguist. 3, 12–21 (2015). (in Chinese)
Yang, E.-H., Li, Y.-Y., Wang, L.: China and the world in popular phrases in 2013. In: Li, Y.-M., Li, W. (eds.) The Language Situation in China 2014, vol. 5, pp. 257–272. De Gruyter Mouton, Berlin (2014)
He, W., Hou, M., Wen, C.-J.: Temporal-spatial modeling for catchwords monitoring. In: The 9th China National Conference on Computational Linguistics, pp. 285–291 (2007). (in Chinese)
Yang, E.-H.: Technology on processing the massive data for language monitoring. Terminol. Standard. Inf. Technol. 2, 38–43 (2010). (In Chinese)
Wu, B.-Z., He, T.-T., Li, L.: Study on popular words and phrases extraction of network based on omni-segmentation. Appl. Res. Comput. 26(4), 1260–1262 (2009). (in Chinese)
InfluxDB 2.0 documentation. https://docs.influxdata.com/influxdb/v2.0/. Accessed 11 Aug 2021
Shang, J.-B., Liu, J.-L., Jiang, M., Ren, X., Voss, C.-R., Han, J.-W.: Automated phrase mining from massive text corpora. IEEE Trans. Knowl. Data Eng 30(10), 1825–1837 (2018)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Wang, Y., Liu, H., Yang, E., Jiang, Y. (2022). Research and Implementation of Buzzword Detection Technology Based on the Dynamic Circulation Corpus. In: Dong, M., Gu, Y., Hong, JF. (eds) Chinese Lexical Semantics. CLSW 2021. Lecture Notes in Computer Science(), vol 13249. Springer, Cham. https://doi.org/10.1007/978-3-031-06703-7_41
Download citation
DOI: https://doi.org/10.1007/978-3-031-06703-7_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-06702-0
Online ISBN: 978-3-031-06703-7
eBook Packages: Computer ScienceComputer Science (R0)