As researchers are writing a draft paper with incomplete structure or text, one of burdensome tasks is to deliberate about which references should be cited for one sentence or paragraph of this draft. In view of the rapid increase in the number of research papers, researchers desire to figure out a better way to do citation recommendations in developing their draft papers. The purpose of this paper is to propose citation recommendation algorithms that enable the acquisition of relevant citations for research papers that are still at the drafting stage. This study attempts to help researchers to select appropriate references among the vast amount of available papers and make draft papers complete in reference citation.
This study adopts a model for recommending citations for incomplete drafts. Four algorithms are proposed in this study. The first and second algorithms are unsupervised models, applying term frequency-inverse document frequency and WordNet technologies, respectively. The third and fourth algorithms are based on the second algorithm to integrate different weight adjustment strategies to improve performance.
The proposed recommendation method adopts three techniques, including using WordNet to transform vector and setting adjustment weights according to structural factors and the information completeness degree, to generate citation recommendation for incomplete drafts. The experiments show that all these three techniques can significantly improve the recommendation accuracy.
None of the methods employed in previous studies can recommend articles as references for incomplete drafts. This paper addresses the situation that a draft paper can be incomplete either in structure or text or both. Recommended references, however, can be still generated and inserted into any desired sentence of the draft paper.
Chen, Y., Weng, C., Huang, C. and Shih, D. (2019), "An innovative citation recommendation model for draft papers with varying degrees of information completeness", Data Technologies and Applications, Vol. 53 No. 4, pp. 562-576. https://doi.org/10.1108/DTA-12-2018-0105Download as .RIS
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