期刊
IEEE ACCESS
卷 9, 期 -, 页码 9982-9995出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3050547
关键词
Metadata; Citation analysis; Computational modeling; Licenses; Context modeling; Task analysis; Semantics; Citation intent; citation analysis; citation context; citation motivation; citation function classification; word embedding; scholarly dataset
资金
- State Key Laboratory of Computer Architecture (ICT, CAS) Open Project [CARCHB202019]
Citation analysis is an active research field, with a shift towards using deep neural network algorithms instead of traditional statistical approaches. Existing scholarly datasets are suitable for statistical methods but lack citation context information, prompting researchers to propose automated techniques for labeling citation intent.
Citation analysis is an active area of research for various reasons. So far, statistical approaches are mainly used for citation analysis, which does not look into the internal context of the citations. Deep analysis of citation may reveal interesting findings by utilizing deep neural network algorithms. The existing scholarly datasets are best suited for statistical approaches but lack citation context, intent, and section information. Furthermore, the datasets are too small to be used with deep learning approaches. For citation intent analysis, the datasets must have a citation context labeled with different citation intent classes. Most of the datasets either do not have labeled context sentences, or the sample is too small to be generalized. In this study, we critically investigated the available datasets for citation intent and proposed an automated citation intent technique to label the citation context with citation intent. Furthermore, we annotated ten million citation contexts with citation intent from Citation Context Dataset (C2D) dataset with the help of our proposed method. We applied Global Vectors (GloVe), Infersent, and Bidirectional Encoder Representations from Transformers (BERT) word embedding methods and compared their Precision, Recall, and F1 measures. It was found that BERT embedding performs significantly better, having an 89% Precision score. The labeled dataset, which is freely available for research purposes, will enhance the study of citation context analysis. Finally, It can be used as a benchmark dataset for finding the citation motivation and function from in-text citations.
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