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A network-based CNN model to identify the hidden information in text data

Author

Listed:
  • Liu, Yanyan
  • Li, Keping
  • Yan, Dongyang
  • Gu, Shuang

Abstract

With the development of the internet and big data, the missing or hidden information identification of text data has become an imperative task. At present, the challenge in the hidden information study is judging whether there is hidden information and where it exists. In this paper, hidden information refers to the words that do not appear in a sentence, however, they have certain correlations with the existing words or sentence and have a great influence on the comprehension of a sentence or part of the text data. This paper focuses on discovering the key and influential hidden information in the text data. A keyword-based hidden information extraction framework is proposed in this paper to search hidden entities, with the assumption that the importance of hidden objects is reflected by the keywords in the text data. A network-based Convolution Neural Network (CNN) model is developed to identify the hidden information related to keywords. The model is based on the results of CNN, and cosine similarity is used to judge whether there is hidden information in the source text data or not. We primarily form the word co-occurrence network of text, select the words with the highest degree as keywords, and generate random walk paths on the network. Besides, we use the random walk path where the last word is the keyword to train CNN. In the experimental section, the proposed model is applied to the dataset in 20Newgroups. The results show that the proposed model can effectively identify the hidden information associated with the keywords in the source text data, and the detection accuracy of keywords can reach 98%–99% achieved by CNN.

Suggested Citation

  • Liu, Yanyan & Li, Keping & Yan, Dongyang & Gu, Shuang, 2022. "A network-based CNN model to identify the hidden information in text data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 590(C).
    • Handle: RePEc:eee:phsmap:v:590:y:2022:i:c:s0378437121009444
    DOI: 10.1016/j.physa.2021.126744
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    References listed on IDEAS

    as
    1. Rybski, Diego & Bunde, Armin, 2009. "On the detection of trends in long-term correlated records," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(8), pages 1687-1695.
    2. Amancio, D.R. & Nunes, M.G.V. & Oliveira, O.N. & Pardo, T.A.S. & Antiqueira, L. & da F. Costa, L., 2011. "Using metrics from complex networks to evaluate machine translation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(1), pages 131-142.
    3. Jamaati, Maryam & Mehri, Ali, 2018. "Text mining by Tsallis entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1368-1376.
    4. Li, Ping & Wang, Bing-Hong, 2007. "Extracting hidden fluctuation patterns of Hang Seng stock index from network topologies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 378(2), pages 519-526.
    5. Assouli, Nora & Benahmed, Khelifa & Gasbaoui, Brahim, 2021. "How to predict crime — informatics-inspired approach from link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    6. Camilo Akimushkin & Diego Raphael Amancio & Osvaldo Novais Oliveira Jr., 2017. "Text Authorship Identified Using the Dynamics of Word Co-Occurrence Networks," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-15, January.
    7. Ramon Ferrer i Cancho & Ricard V. Solé, 2001. "The Small-World of Human Language," Working Papers 01-03-016, Santa Fe Institute.
    8. Antiqueira, L. & Nunes, M.G.V. & Oliveira Jr., O.N. & F. Costa, L. da, 2007. "Strong correlations between text quality and complex networks features," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 373(C), pages 811-820.
    9. Tohalino, Jorge V. & Amancio, Diego R., 2018. "Extractive multi-document summarization using multilayer networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 526-539.
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    Cited by:

    1. Yanyan Liu & Keping Li & Dongyang Yan & Shuang Gu, 2023. "The prediction of disaster risk paths based on IECNN model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(1), pages 163-188, May.

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