IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v11y2019i1p17-d197413.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/11/1/17/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1999-5903/11/1/17/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ingrid E. Fisher & Margaret R. Garnsey & Mark E. Hughes, 2016. "Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(3), pages 157-214, July.
    2. Gehan A. Mousa & Elsayed A. H. Elamir & Khaled Hussainey, 2022. "Using machine learning methods to predict financial performance: Does disclosure tone matter?," International Journal of Disclosure and Governance, Palgrave Macmillan, vol. 19(1), pages 93-112, March.
    3. Stolowy, Hervé & Jeanjean, Thomas & Erkens, Michael, 2011. "The economic consequences of increasing the international visibility of financial reports," HEC Research Papers Series 957, HEC Paris.
    4. Yan Luo & Linying Zhou, 2020. "Textual tone in corporate financial disclosures: a survey of the literature," International Journal of Disclosure and Governance, Palgrave Macmillan, vol. 17(2), pages 101-110, September.
    5. Jiao Ji & Oleksandr Talavera & Shuxing Yin, 2018. "The Hidden Information Content: Evidence from the Tone of Independent Director Reports," Working Papers 2018-28, Swansea University, School of Management.
    6. Kamaladdin Fataliyev & Aneesh Chivukula & Mukesh Prasad & Wei Liu, 2021. "Stock Market Analysis with Text Data: A Review," Papers 2106.12985, arXiv.org, revised Jul 2021.
    7. Everett, Jeff & Shiraz Rahaman, Abu & Neu, Dean & Saxton, Gregory, 2024. "Letters to the editor, institutional experimentation, and the public accounting professional," CRITICAL PERSPECTIVES ON ACCOUNTING, Elsevier, vol. 99(C).
    8. Alastair Marais, 2024. "Audit Quality and Financial Statement Manipulation: The Moderating Effect of Tone at the Top," International Journal of Economics and Financial Issues, Econjournals, vol. 14(5), pages 220-232, September.
    9. Lu Zhang & Yuan George Shan & Millicent Chang, 2021. "Can CSR Disclosure Protect Firm Reputation During Financial Restatements?," Journal of Business Ethics, Springer, vol. 173(1), pages 157-184, September.
    10. Yingying Xin & Xiao Zeng & Zhengying Luo, 2022. "Customers' tone in MD&A disclosure and suppliers' inventory efficiency: Evidence from China," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(8), pages 3833-3853, December.
    11. Liu, Pu & Nguyen, Hazel T., 2020. "CEO characteristics and tone at the top inconsistency," Journal of Economics and Business, Elsevier, vol. 108(C).
    12. Ahmed, Yousry & Elshandidy, Tamer, 2016. "The effect of bidder conservatism on M&A decisions: Text-based evidence from US 10-K filings," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 176-190.
    13. Michael J Bommarito II & Daniel Martin Katz, 2016. "Measuring the temperature and diversity of the U.S. regulatory ecosystem," Papers 1612.09244, arXiv.org, revised Jan 2017.
    14. Bingler, Julia Anna & Kraus, Mathias & Leippold, Markus & Webersinke, Nicolas, 2024. "How cheap talk in climate disclosures relates to climate initiatives, corporate emissions, and reputation risk," Journal of Banking & Finance, Elsevier, vol. 164(C).
    15. Liebmann, Michael & Orlov, Alexei G. & Neumann, Dirk, 2016. "The tone of financial news and the perceptions of stock and CDS traders," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 159-175.
    16. Dongshin Kim & Dongkuk Lim & Jonathan A. Wiley, 2023. "Narrative Investment-Risk Disclosure & REIT Investment," The Journal of Real Estate Finance and Economics, Springer, vol. 66(2), pages 542-567, February.
    17. Thomas Renault, 2020. "Sentiment analysis and machine learning in finance: a comparison of methods and models on one million messages," Digital Finance, Springer, vol. 2(1), pages 1-13, September.
    18. Allen H. Huang & Jianghua Shen & Amy Y. Zang, 2022. "The unintended benefit of the risk factor mandate of 2005," Review of Accounting Studies, Springer, vol. 27(4), pages 1319-1355, December.
    19. Özgür Arslan‐Ayaydin & James Thewissen & Wouter Torsin, 2021. "Disclosure tone management and labor unions," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 48(1-2), pages 102-147, January.
    20. Sheng-Syan Chen & Chia-Wei Huang & Chuan-Yang Hwang & Yanzhi Wang, 2022. "Voluntary disclosure and corporate innovation," Review of Quantitative Finance and Accounting, Springer, vol. 58(3), pages 1081-1115, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:11:y:2019:i:1:p:17-:d:197413. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.