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Citation Intent Classification Using Word Embedding

文献类型：期刊论文

作者Roman, Muhammad2; Shahid, Abdul2; Khan, Shafiullah2; Koubaa, Anis1,3; Yu, Lisu4,5
刊名IEEE ACCESS
出版日期2021
卷号9页码:9982-9995
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
ISSN号2169-3536
DOI10.1109/ACCESS.2021.3050547
英文摘要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.
资助项目State Key Laboratory of Computer Architecture (ICT, CAS) Open Project[CARCHB202019]
WOS研究方向Computer Science ; Engineering ; Telecommunications
语种英语
WOS记录号WOS:000609801100001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/16327]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Yu, Lisu
作者单位1.Polytech Inst Porto, CISTER INESC TEC, P-4200 Porto, Portugal
2.Kohat Univ Sci & Technol, Inst Comp, Kohat 26000, Pakistan
3.Prince Sultan Univ, Robot & Internet Things Lab, Riyadh 12435, Saudi Arabia
4.Nanchang Univ, Sch Informat Engn, Nanchang 330031, Jiangxi, Peoples R China
5.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Roman, Muhammad,Shahid, Abdul,Khan, Shafiullah,et al. Citation Intent Classification Using Word Embedding[J]. IEEE ACCESS,2021,9:9982-9995.
APA Roman, Muhammad,Shahid, Abdul,Khan, Shafiullah,Koubaa, Anis,&Yu, Lisu.(2021).Citation Intent Classification Using Word Embedding.IEEE ACCESS,9,9982-9995.
MLA Roman, Muhammad,et al."Citation Intent Classification Using Word Embedding".IEEE ACCESS 9(2021):9982-9995.

入库方式： OAI收割

来源：计算技术研究所

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