IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v127y2022i2d10.1007_s11192-021-04225-1.html
   My bibliography  Save this article

Enhancing identification of structure function of academic articles using contextual information

Author

Listed:
  • Bowen Ma

    (Nanjing University)

  • Chengzhi Zhang

    (Nanjing University of Science and Technology)

  • Yuzhuo Wang

    (Nanjing University of Science and Technology)

  • Sanhong Deng

    (Nanjing University)

Abstract

With the enrichment of literature resources, researchers are facing the growing problem of information explosion and knowledge overload. To help scholars retrieve literature and acquire knowledge successfully, clarifying the semantic structure of the content in academic literature has become the essential research question. In the research on identifying the structure function of chapters in academic articles, only a few studies used the deep learning model and explored the optimization for feature input. This limits the application, optimization potential of deep learning models for the research task. This paper took articles of the ACL conference as the corpus. We employ the traditional machine learning models and deep learning models to construct the classifiers based on various feature input. Experimental results show that (1) Compared with the chapter content, the chapter title is more conducive to identifying the structure function of academic articles. (2) Relative position is a valuable feature for building traditional models. (3) Inspired by (2), this paper further introduces contextual information into the deep learning models and achieved significant results. Meanwhile, our models show good migration ability in the open test containing 200 sampled non-training samples. We also annotated the ACL main conference papers in recent five years based on the best practice performing models and performed a time series analysis of the overall corpus. This work explores and summarizes the practical features and models for this task through multiple comparative experiments and provides a reference for related text classification tasks. Finally, we indicate the limitations and shortcomings of the current model and the direction of further optimization.

Suggested Citation

  • Bowen Ma & Chengzhi Zhang & Yuzhuo Wang & Sanhong Deng, 2022. "Enhancing identification of structure function of academic articles using contextual information," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(2), pages 885-925, February.
  • Handle: RePEc:spr:scient:v:127:y:2022:i:2:d:10.1007_s11192-021-04225-1
    DOI: 10.1007/s11192-021-04225-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-021-04225-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11192-021-04225-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ding, Ying & Liu, Xiaozhong & Guo, Chun & Cronin, Blaise, 2013. "The distribution of references across texts: Some implications for citation analysis," Journal of Informetrics, Elsevier, vol. 7(3), pages 583-592.
    2. Raja Habib & Muhammad Tanvir Afzal, 2019. "Sections-based bibliographic coupling for research paper recommendation," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 643-656, May.
    3. Hu, Zhigang & Chen, Chaomei & Liu, Zeyuan, 2013. "Where are citations located in the body of scientific articles? A study of the distributions of citation locations," Journal of Informetrics, Elsevier, vol. 7(4), pages 887-896.
    4. Marc Bertin & Iana Atanassova & Cassidy R. Sugimoto & Vincent Lariviere, 2016. "The linguistic patterns and rhetorical structure of citation context: an approach using n-grams," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 1417-1434, December.
    5. Mercedes Echeverria & David Stuart & Tobias Blanke, 2015. "Medical theses and derivative articles: dissemination of contents and publication patterns," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 559-586, January.
    6. Xiaodan Zhu & Peter Turney & Daniel Lemire & André Vellino, 2015. "Measuring academic influence: Not all citations are equal," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(2), pages 408-427, February.
    7. Kevin Heffernan & Simone Teufel, 2018. "Identifying problems and solutions in scientific text," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 1367-1382, August.
    8. Nasrin Asadi & Kambiz Badie & Maryam Tayefeh Mahmoudi, 2019. "Automatic zone identification in scientific papers via fusion techniques," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 845-862, May.
    9. Chao Lu & Ying Ding & Chengzhi Zhang, 2017. "Understanding the impact change of a highly cited article: a content-based citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(2), pages 927-945, August.
    10. Wei Lu & Yong Huang & Yi Bu & Qikai Cheng, 2018. "Functional structure identification of scientific documents in computer science," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 463-486, April.
    Full references (including those not matched with items on IDEAS)

    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. Shengzhi Huang & Jiajia Qian & Yong Huang & Wei Lu & Yi Bu & Jinqing Yang & Qikai Cheng, 2022. "Disclosing the relationship between citation structure and future impact of a publication," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(7), pages 1025-1042, July.
    2. Hamid R. Jamali & Majid Nabavi & Saeid Asadi, 2018. "How video articles are cited, the case of JoVE: Journal of Visualized Experiments," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1821-1839, December.
    3. Wang, Shiyun & Mao, Jin & Lu, Kun & Cao, Yujie & Li, Gang, 2021. "Understanding interdisciplinary knowledge integration through citance analysis: A case study on eHealth," Journal of Informetrics, Elsevier, vol. 15(4).
    4. Ruhao Zhang & Junpeng Yuan, 2022. "Enhanced author bibliographic coupling analysis using semantic and syntactic citation information," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7681-7706, December.
    5. Mingyang Wang & Jiaqi Zhang & Shijia Jiao & Xiangrong Zhang & Na Zhu & Guangsheng Chen, 2020. "Important citation identification by exploiting the syntactic and contextual information of citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2109-2129, December.
    6. Liyue Chen & Jielan Ding & Vincent Larivière, 2022. "Measuring the citation context of national self‐references," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(5), pages 671-686, May.
    7. CholMyong Pak & Guang Yu & Weibin Wang, 2018. "A study on the citation situation within the citing paper: citation distribution of references according to mention frequency," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 905-918, March.
    8. Waltman, Ludo, 2016. "A review of the literature on citation impact indicators," Journal of Informetrics, Elsevier, vol. 10(2), pages 365-391.
    9. Weibin Wang & Zheng Wang & Tian Yu & CholMyong Pak & Guang Yu, 2020. "Research on citation mention times and contributions using a neural network," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2383-2400, December.
    10. Matthias Sebastian Rüdiger & David Antons & Torsten-Oliver Salge, 2021. "The explanatory power of citations: a new approach to unpacking impact in science," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9779-9809, December.
    11. Muhammad Touseef Ikram & Muhammad Tanvir Afzal, 2019. "Aspect based citation sentiment analysis using linguistic patterns for better comprehension of scientific knowledge," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 73-95, April.
    12. Boyack, Kevin W. & van Eck, Nees Jan & Colavizza, Giovanni & Waltman, Ludo, 2018. "Characterizing in-text citations in scientific articles: A large-scale analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 59-73.
    13. Aurora González-Teruel & Francisca Abad-García, 2018. "The influence of Elfreda Chatman’s theories: a citation context analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1793-1819, December.
    14. Sehrish Iqbal & Saeed-Ul Hassan & Naif Radi Aljohani & Salem Alelyani & Raheel Nawaz & Lutz Bornmann, 2021. "A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6551-6599, August.
    15. Toluwase Victor Asubiaro & Isola Ajiferuke, 2022. "Semantic similarity-based credit attribution on citation paths: a method for allocating residual citation to and investigating depth of influence of scientific communications," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6257-6277, November.
    16. Martin Ricker, 2017. "Letter to the Editor: About the quality and impact of scientific articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1851-1855, June.
    17. Yi Bu & Binglu Wang & Win-bin Huang & Shangkun Che & Yong Huang, 2018. "Using the appearance of citations in full text on author co-citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 275-289, July.
    18. Naif Radi Aljohani & Ayman Fayoumi & Saeed-Ul Hassan, 2021. "An in-text citation classification predictive model for a scholarly search system," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5509-5529, July.
    19. Yuzhuo Wang & Chengzhi Zhang & Kai Li, 2022. "A review on method entities in the academic literature: extraction, evaluation, and application," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2479-2520, May.
    20. John P A Ioannidis & Kevin Boyack & Paul F Wouters, 2016. "Citation Metrics: A Primer on How (Not) to Normalize," PLOS Biology, Public Library of Science, vol. 14(9), pages 1-7, September.

    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:spr:scient:v:127:y:2022:i:2:d:10.1007_s11192-021-04225-1. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.