淡江大學機構典藏:Item 987654321/126326
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    题名: ICRM: An Intelligent Citation Recommendation Mechanism Based on BERT and Weighted BoW Models
    作者: Chang, Chih-Yunga;Yang, Yu-Tinga;Zhang, Qiaoyuna;Lin, Yi-Tib;Roy, Diptendu Sinhac
    关键词: Citation recommendation;TF-IDF;weighted bag of word;BERT
    日期: 2024-04-18
    上传时间: 2024-10-02 12:05:40 (UTC+8)
    摘要: With the field of technology has witnessed rapid advancements, attracting an ever-growing community of researchers dedicated to developing theories and techniques. This paper proposes an innovative ICRM (Intelligent Citation Recommendation Mechanism), designed to automate the process of suggesting the appropriate number of citations for individual brackets within a document. The proposed ICRM comprises three phases: Coarse-grained Weighted Bag of Word (WCBW), Fine-grained SciBERT (FSB) and Citation Adjustment phases. Firstly, the WCBW phase employs TF-IDF to extract keywords from both target and candidate documents, forming vectors that capture word significance along with metadata like authorship, keywords, and titles. It aims to identify relevant papers from a database, serving as initial candidates for each bracket. Secondly, the FSB phase employs the SciBERT model to assess the similarity between candidate documents and the local context around brackets, enhancing the precision of recommendations. It refines this selection by analyzing candidate-document relationships within the proximity of the brackets. Lastly, the Citation Adjustment phase tackles overlapping citations and ensures that recommended citation numbers align with user-defined criteria, resolving issues of imbalance. The simulation results demonstrate that the proposed ICRM outperforms existing models significantly in terms of precision, recall and F1-score.
    關聯: Journal of Intelligent & Fuzzy Systems , vol. 46, pp. 10135-10150
    DOI: 10.3233/JIFS-237975
    显示于类别:[資訊工程學系暨研究所] 期刊論文

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