Identifying multidisciplinary problems from scientific publications based on a text generation method
Article Category: Research Papers
Publié en ligne: 25 juil. 2024
Pages: -
Reçu: 21 mars 2024
Accepté: 28 juin 2024
DOI: https://doi.org/10.2478/jdis-2024-0021
Mots clés
© 2024 Ziyan Xu et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
Purpose
A text generation based multidisciplinary problem identification method is proposed, which does not rely on a large amount of data annotation.
Design/methodology/approach
The proposed method first identifies the research objective types and disciplinary labels of papers using a text classification technique; second, it generates abstractive titles for each paper based on abstract and research objective types using a generative pre-trained language model; third, it extracts problem phrases from generated titles according to regular expression rules; fourth, it creates problem relation networks and identifies the same problems by exploiting a weighted community detection algorithm; finally, it identifies multidisciplinary problems based on the disciplinary labels of papers.
Findings
Experiments in the “Carbon Peaking and Carbon Neutrality” field show that the proposed method can effectively identify multidisciplinary research problems. The disciplinary distribution of the identified problems is consistent with our understanding of multidisciplinary collaboration in the field.
Research limitations
It is necessary to use the proposed method in other multidisciplinary fields to validate its effectiveness.
Practical implications
Multidisciplinary problem identification helps to gather multidisciplinary forces to solve complex real-world problems for the governments, fund valuable multidisciplinary problems for research management authorities, and borrow ideas from other disciplines for researchers.
Originality/value
This approach proposes a novel multidisciplinary problem identification method based on text generation, which identifies multidisciplinary problems based on generative abstractive titles of papers without data annotation required by standard sequence labeling techniques.