Abstract
Disambiguating various people that share the same name is a critical issue for analyzing contents in online medium. This paper develops a framework for dealing with personal names in Chinese dataset. Web pages containing personal name are crawled from the online website and standardized at first. Then documents are parsed with lexical analysis technologies, such as segmentation, part-of-speech tagging, named entity recognition. We extract several groups of words as features, testing different weighting schemes (e.g. Boolean term frequency, absolute term frequency, tf-idf, entropy weights). By conducting the agglomerative clustering, a measure of interdependence within clusters and independence between clusters is proposed for automatically determining the number of clusters. Moreover, a technique that merges noise clusters is utilized to improve the clustering results. Experiments are performed on six groups of Chinese personal names and the final results confirm our proposed approach.
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Li, G., Wang, H.: Chinese named entity recognition and disambiguation based on multi-stage clustering. J. Chin. Inf. Process. 27(5), 29–34 (2013)
Zhao, L., Yan, Z., Liang, H.: Ontology-based personal name disambiguation on the web. In: Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), vol. 01 (2013)
Emami, H.: A graph-based approach to person name disambiguation in web. ACM Trans. Manag. Inf. Syst. 10(2), 4.1–4.25 (2019)
Xiong, L., et al.: Chinese name disambiguation based on analysis of sentential semantic structure. Appl. Res. Comput. 33(10), 2898–2901 (2016)
Yang, Y., Zhou, J., Li, B.: Name disambiguation algorithm based on ensemble. Appl. Res. Comput. 33(9), 2716–2720 (2016)
Zeng, J., et al.: Research on expert disambiguation of same name based on multi-feature fusion. Acta Scientiarum Naturalium Universitatis Pekinensis 56(4), 607–613 (2020)
Shang, Y., et al.: Co-author and afiliate based name disambiguation approach. Comput. Sci. 45(11), 220–225 (2018)
Zhai, X., et al.: Research on English author name disambiguation based on sparse distributed representation. Appl. Res. Comput. 36(12), 3534–3538 (2019)
Yu, C., et al.: Author name disambiguation with network embedding. Data Anal. Knowl. Disc. 4(2/3), 48–59 (2020)
Pooja, K.M., Mondal, S., Chandra, J.: A graph combination with edge pruning-based approach for author name disambiguation. J. Am. Soc. Inf. Sci. 71(1), 69–83 (2020)
Kim, K., Rohatgi, S., Giles, C.L.: Hybrid deep pairwise classification for author name disambiguation. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 2369–2372 (2019)
Delgado, D., et al.: Person name disambiguation on the web in a multilingual context. Inf. Sci. 465, 373–387 (2018)
Khabsa, M., Treeratpituk, P., Giles, C.L.: Online person name disambiguation with constraints. In: Proceedings of the 15th ACM/IEEE-CS Joint Conference on Digital Libraries, Knoxville, TN, pp. 37–46 (2015)
Hotho, A., Staab, S., Stumme, G.: WordNet improves text document clustering. In: Proceedings of the SIGIR 2003 Semantic Web Workshop, pp. 541–544 (2003)
Newman, M.E.J.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577–8582 (2006)
Fan, C., Toriumi, F.: High-modularity network generation model based on the muitilayer network. Trans. Jpn. Soc. Artif. Intell. 32(6), B-H42_1-11 (2017). https://doi.org/10.1527/tjsai.B-H42
Acknowledgement
This work was supported by the Youth Foundation of Basic Science Research Program of Jiangnan University, 2019 (No. JUSRP11962) and High-level Innovation and Entrepreneurship Talents Introduction Program of Jiangsu Province of China, 2019.
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Fan, C., Li, Y. (2021). Personal Name Disambiguation for Chinese Documents in Online Medium. In: Fu, W., Xu, Y., Wang, SH., Zhang, Y. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 387. Springer, Cham. https://doi.org/10.1007/978-3-030-82562-1_23
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DOI: https://doi.org/10.1007/978-3-030-82562-1_23
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