An Association Rule Mining Method Based on Named Entity Recognition and Text Classification
Author:
Funder
Humanities and Social Sciences of Ministry Education of China
Publisher
Springer Science and Business Media LLC
Subject
Multidisciplinary
Link
https://link.springer.com/content/pdf/10.1007/s13369-022-06870-x.pdf
Reference22 articles.
1. Xu, C.W.; Wang, F.Y.; Han, J.L; et al.: Exploiting multiple embeddings for chinese named entity recognition. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM '19), pp. 2269–2272 (2019). https://doi.org/10.1145/3357384.3358117
2. Liu, W.M.; Yu, B.; Zhang, C.; et al.: Chinese named entity recognition based on rules and conditional random field. In Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence (CSAI '18), pp. 268–272(2018). https://doi.org/10.1145/3297156.3297196
3. Asif, E.; Sriparna, S.; Dhirendra, S.: Active machine learning technique for named entity recognition. In Proceedings of the International Conference on Advances in Computing, Communications and Informatics (ICACCI '12), pp. 180–186 (2012). https://doi.org/10.1145/2345396.2345427
4. Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257–286 (1989). https://doi.org/10.1109/5.18626
5. Yu, H.K.; Zhang, H.P.; Liu, Q.; et al.: Chinese named entity identification using cascaded hidden Markov model. J.-China Instit. Commun. 27(2), 87–94 (2006). https://doi.org/10.3321/j.issn:1000-436X.2006.02.013
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