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An Association Rule Mining Method Based on Named Entity Recognition and Text Classification

  • Research Article-Computer Engineering and Computer Science
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Abstract

Using massive text data, building a knowledge graph to implement in-depth association analysis and mining can help identify entities and make decisions. The accuracy of traditional Chinese Named Entity Recognition methods is low, and traditional frequent itemset mining methods are also difficult to obtain different types of categories, and their novelty is not high. In this paper, we propose an association rule mining method based on named entity recognition and text classification (ARMTNER). First, the TextCNN model is used to extract the word vector information of the text data; secondly, bidirectional LSTM is used the model extracts the contextual features of the text; then the neural network model is used to automatically extract the word features and the global features of the text for text classification; finally, the text sequence labeling and entity recognition are performed. Then the association rules be used to mine frequent itemsets through two-level classification of text classification and entity classification. The experimental results showed that, our method can achieve an F1-score of 97.3% on the public data set in Chinese Named Entity Recognition, and the novelty of frequent itemsets increased by 0.279%.

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Acknowledgements

This research is supported by the Humanities and Social Sciences of Ministry Education of China (No.19XJA910001) and the postgraduate innovation fund project of Chongqing University of Technology (No.clgycx 20203114).

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Authors and Affiliations

  1. School of Computer Science and Engineering, ChongQing University of Technology, Chongqing, 400054, China

    Bo He & Jiru Zhang

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  1. Bo He
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  2. Jiru Zhang
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Correspondence to Jiru Zhang.

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He, B., Zhang, J. An Association Rule Mining Method Based on Named Entity Recognition and Text Classification. Arab J Sci Eng 48, 1503–1511 (2023). https://doi.org/10.1007/s13369-022-06870-x

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