Semantic Relationship Study between Citing and Cited Scientific Articles Using Topic Modeling

Mifrah, Sara; Benlahmar, El Habib

doi:10.1145/3372938.3372943

Cited by 2 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our evaluation approach based on the evaluation of the quality of the content of a scientific article, we added two other criteria: (1) the subject treated by the citing article and its relationship with the other cited (Topic Modeling) [10] and (2) the purpose of the citation (Sentiment Analysis).…”

Section: Methodsmentioning

confidence: 99%

Toward a Semantic Graph of Scientific Publications: A Bibliometric Study

Mifrah¹

2020

IJATCSE

Self Cite

View full text Add to dashboard Cite

The term "scientific publication" includes several types of scientific communications and digital broadcasts that scientific researchers make of their work towards their peers and an audience of specialists. These publications describe in detail the studies or experiments carried out and the conclusions drawn from them by the authors. They undergo an examination of the value of the results and the rigor of the scientific method used for the work carried out. In this paper we evaluated the quality of a scientific article on the subject (topic), based on its citations and where is it cited, we based on the Topic modeling theme with the choice of LDA algorithms applied to the corpus Nips for detecting all subjects of each paper and there citations in a first step then on the citations of each article of the corpus and on the Sentiment Analysis using a lexical based approaches. Then we created a csv file containing the link of each paper with the other cited papers (relation cited-citing), and finally generated a semantic graph between these publications.

show abstract

Section: Methodsmentioning

confidence: 99%

Toward a Semantic Graph of Scientific Publications: A Bibliometric Study

Mifrah¹

2020

IJATCSE

Self Cite

View full text Add to dashboard Cite

show abstract

“…[1] [2] [3] [4] It was found that the 2019 novel coronavirus (2019-nCoV) was the cause of unexplained viral pneumonia in Wuhan China in December 2019 and was recognized by the World Health Organization (WHO) on January 12 2020. 1010 https://www.ncbi.nlm.n ih.gov/pmc/articles/PM C7144984/ ncbi.nlm.nih.gov For deeper investigation to find the role that bats play as MERS-CoV risk factor of viral transmission more research is needed [4].…”

mentioning

confidence: 99%

Topic Modeling with Transformers for Sentence-Level Using Coronavirus Corpus

Mifrah

Benlahmar²

2022

Int. J. Interact. Mob. Technol.

Self Cite

View full text Add to dashboard Cite

A Topic Model is a class of generative probabilistic models which has gained widespread use in computer science in recent years, especially in the field of text mining and information retrieval. Since it was first proposed, it has received a large amount of attention and general interest among scientists in many research areas. It allows us to discover the mix of hidden or "latent" subjects that differs from one document to another in a given corpus. But since topic modeling usually requires the prior definition of some parameters - above all the number of topics k to be discovered -, model evaluation is decisive to identify an "optimal" set of parameters for the specific data. Latent Dirichlet allocation (LDA) and Bidirectional Encoder Representations from Transformers Topic (BerTopic) are the two most popular topic modeling techniques. LDA uses a probabilistic approach whereas BerTopic uses transformers (BERT embeddings) and class-based TF-IDF to create dense clusters.

show abstract

Semantic Relationship Study between Citing and Cited Scientific Articles Using Topic Modeling

Cited by 2 publications

References 6 publications

Toward a Semantic Graph of Scientific Publications: A Bibliometric Study

Toward a Semantic Graph of Scientific Publications: A Bibliometric Study

Topic Modeling with Transformers for Sentence-Level Using Coronavirus Corpus

Contact Info

Product

Resources

About