Multi-label classification of research articles using Word2Vec and identification of similarity threshold

doi:10.1038/s41598-021-01460-7

	Multi-label classification of research articles using Word2Vec and identification of similarity threshold
	Mustafa, Ghulam 1; Usman, Muhammad 2; Yu, Lisu 3,4; Afzal, Muhammad Tanvir 5; Sulaiman, Muhammad 1; Shahid, Abdul 6
	2021-11-09
发表期刊	SCIENTIFIC REPORTS
ISSN	2045-2322
卷号	11 期号:1 页码:20
摘要	Every year, around 28,100 journals publish 2.5 million research publications. Search engines, digital libraries, and citation indexes are used extensively to search these publications. When a user submits a query, it generates a large number of documents among which just a few are relevant. Due to inadequate indexing, the resultant documents are largely unstructured. Publicly known systems mostly index the research papers using keywords rather than using subject hierarchy. Numerous methods reported for performing single-label classification (SLC) or multi-label classification (MLC) are based on content and metadata features. Content-based techniques offer higher outcomes due to the extreme richness of features. But the drawback of content-based techniques is the unavailability of full text in most cases. The use of metadata-based parameters, such as title, keywords, and general terms, acts as an alternative to content. However, existing metadata-based techniques indicate low accuracy due to the use of traditional statistical measures to express textual properties in quantitative form, such as BOW, TF, and TFIDF. These measures may not establish the semantic context of the words. The existing MLC techniques require a specified threshold value to map articles into predetermined categories for which domain knowledge is necessary. The objective of this paper is to get over the limitations of SLC and MLC techniques. To capture the semantic and contextual information of words, the suggested approach leverages the Word2Vec paradigm for textual representation. The suggested model determines threshold values using rigorous data analysis, obviating the necessity for domain expertise. Experimentation is carried out on two datasets from the field of computer science (JUCS and ACM). In comparison to current state-of-the-art methodologies, the proposed model performed well. Experiments yielded average accuracy of 0.86 and 0.84 for JUCS and ACM for SLC, and 0.81 and 0.80 for JUCS and ACM for MLC. On both datasets, the proposed SLC model improved the accuracy up to 4%, while the proposed MLC model increased the accuracy up to 3%.
DOI	10.1038/s41598-021-01460-7
收录类别	SCI
语种	英语
资助项目	National Science Foundation of China (NSFC)[62161024] ; China Postdoctoral Science Foundation[2021TQ0136] ; State Key Laboratory of Computer Architecture (ICT, CAS) Open Project[CARCHB202019]
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:000716666700024
出版者	NATURE PORTFOLIO
引用统计	被引频次：16[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/18052
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.Capital Univ Sci & Technol, Dept Comp Sci, Islamabad 44000, Pakistan 2.Natl Univ Comp & Emerging Sci FAST, Dept Comp Sci, Islamabad 44000, Pakistan 3.Nanchang Univ, Sch Informat Engn, Nanchang 330031, Jiangxi, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China 5.Namal Inst, Dept Comp Sci, Islamabad 42200, Pakistan 6.Kohat Univ Sci & Technol, Inst Comp, Kohat 26000, Pakistan
推荐引用方式 GB/T 7714	Mustafa, Ghulam,Usman, Muhammad,Yu, Lisu,et al. Multi-label classification of research articles using Word2Vec and identification of similarity threshold[J]. SCIENTIFIC REPORTS,2021,11(1):20.
APA	Mustafa, Ghulam,Usman, Muhammad,Yu, Lisu,Afzal, Muhammad Tanvir,Sulaiman, Muhammad,&Shahid, Abdul.(2021).Multi-label classification of research articles using Word2Vec and identification of similarity threshold.SCIENTIFIC REPORTS,11(1),20.
MLA	Mustafa, Ghulam,et al."Multi-label classification of research articles using Word2Vec and identification of similarity threshold".SCIENTIFIC REPORTS 11.1(2021):20.