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Forward-Looking Element Recognition Based on the LSTM-CRF Model with the Integrity Algorithm

Author

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  • Dong Xu

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Ruping Ge

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Zhihua Niu

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

Abstract

A state-of-the-art entity recognition system relies on deep learning under data-driven conditions. In this paper, we combine deep learning with linguistic features and propose the long short-term memory-conditional random field model (LSTM-CRF model) with the integrity algorithm. This approach is primarily based on the use of part-of-speech (POS) syntactic rules to correct the boundaries of LSTM-CRF model annotations and improve its performance by raising the integrity of the elements. The method incorporates the advantages of the data-driven method and dependency syntax, and improves the precision rate of the elements without losing recall rate. Experiments show that the integrity algorithm is not only easy to combine with the other neural network model, but the overall effect is better than several advanced methods. In addition, we conducted cross-domain experiments based on a multi-industry corpus in the financial field. The results indicate that the method can be applied to other industries.

Suggested Citation

  • Dong Xu & Ruping Ge & Zhihua Niu, 2019. "Forward-Looking Element Recognition Based on the LSTM-CRF Model with the Integrity Algorithm," Future Internet, MDPI, vol. 11(1), pages 1-16, January.
    • Handle: RePEc:gam:jftint:v:11:y:2019:i:1:p:17-:d:197413
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    References listed on IDEAS

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    1. Xin Ying Qiu & Padmini Srinivasan & Yong Hu, 2014. "Supervised learning models to predict firm performance with annual reports: An empirical study," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(2), pages 400-413, February.
    2. Feng Li, 2010. "The Information Content of Forward‐Looking Statements in Corporate Filings—A Naïve Bayesian Machine Learning Approach," Journal of Accounting Research, Wiley Blackwell, vol. 48(5), pages 1049-1102, December.
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    Cited by:

    1. Salvatore Graziani & Maria Gabriella Xibilia, 2020. "Innovative Topologies and Algorithms for Neural Networks," Future Internet, MDPI, vol. 12(7), pages 1-4, July.

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