Identifying Important Citations Using Contextual Information from Full Text

Hassan, Saeed‐Ul; Akram, Anam; Haddawy, Peter

doi:10.1109/jcdl.2017.7991558

Cited by 44 publications

(39 citation statements)

References 6 publications

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“…Figure 12 shows the results for COMST and TON data, where the number of references in COMST and TON goes up with the increasing number of authors. Similar results are reported by Saeed et al, Valenzuela et al and Zhu et al in their studies (Hassan et al 2017a;Valenzuela et al 2015;Zhu et al 2015). Figure 12 also shows that with the increasing number of authors, number of references from the last ten years in a paper also increase in COMST and TON.…”

Section: Analysis Based On Structural Elements Of Articlesupporting

confidence: 85%

A bibliometric analysis of publications in computer networking research

et al. 2019

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Computer networking is a major research discipline in computer science, electrical engineering, and computer engineering. The field has been actively growing, in terms of both research and development, for the past hundred years. This study uses the article content and metadata of four important computer networking periodicals) to address important bibliometrics questions. All of the venues are prestigious, yet they publish quite different research. The first two of these periodicals (COMST and TON) are highly reputed journals of the fields while SIGCOMM and INFOCOM are considered top conferences of the field. SIG-COMM and INFOCOM publish new original research. TON has a similar genre and publishes new original research as well as the extended versions of different research published in the conferences such as SIGCOMM and INFOCOM, while COMST publishes surveys and reviews (which not only summarize previous works but highlight future research opportunities). In this study, we aim to track the co-evolution of trends in the COMST and TON journals and compare them to the publication trends in INFOCOM and SIGCOMM. Our analyses of the computer networking literature include: (a) metadata analysis; (b) content-based analysis; and (c) citation analysis. In addition, we identify

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Section: Analysis Based On Structural Elements Of Articlesupporting

confidence: 85%

A bibliometric analysis of publications in computer networking research

et al. 2019

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“…In addition, we found that qualitative assessment helps better understand the feature set being examined. A potential limitation of this study is the adoption of the definitions that as such came with the dataset [4,14]. In future studies, other definitions and features could be explored, such as stylometric features from full-text [15].…”

Section: Discussionmentioning

confidence: 99%

Mining the Context of Citations in Scientific Publications

Hassan

Iqbal

Imran

et al. 2018

Lecture Notes in Computer Science

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Recent advancements in information retrieval systems significantly rely on the context-based features and semantic matching techniques to provide relevant information to users from ever-growing digital libraries. Scientific communities seek to understand the implications of research, its importance and its applicability for future research directions. To mine this information, absolute citations merely fail to measure the importance of scientific literature, as a citation may have a specific context in full text. Thus, a comprehensive contextual understanding of cited references is necessary. For this purpose, numerous techniques have been proposed that tap the power of artificial intelligence models to detect important or incidental (non-important) citations in full text scholarly publications. In this paper, we compare and build upon on four state-of-the-art models that detect important citations using 450 manually annotated citations by experts-randomly selected from 20,527 papers from the Association for Computational Linguistics corpus. Of the total 64 unique features proposed by the four selected state-of-the-art models, the top 29 were chosen using the Extra-Trees classifier. These were then fed it to our supervised machine learning based models: Random Forest (RF) and Support Vector Machine. The RF model outperforms existing selected systems by more than 10%, with 89% precision-recall curve. Finally, we qualitatively assessed important and non-important citations by employing and self-organizing maps. Overall, our research work supports information retrieval algorithms that detect and fetch scientific articles on the basis of both qualitative and quantitative indices in scholarly big data.

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“…This method uses the entire dataset and draws random splits (decision trees) for each of the randomly selected features, and then the best division is chosen. For each tree, the importance is calculated from the impurity of the splits, obtaining a higher value where the characteristics are more important [14]. The separations are random to ensure that the model does not overfit the data.…”

Section: Feature Importance Methodsmentioning

confidence: 99%

Students Drop Out Trends: A University Study

Silva

Pires

Reis

et al. 2021

Communications in Computer and Information Science

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The dropout of university students has been a factor of concern for educational institutions, affecting various aspects such as the institution's reputation and funding and rankings. For this reason, it is essential to identify which students are at risk . In this study, algorithms based on decision trees and random forests are proposed to solve these problems using real data from 331 students from the University of Trásos-Montes and Alto Douro. In this work with these learning algorithms together with the training strategies , we managed to obtain an 89% forecast of students who may abandon their studies based on the evaluations of both semesters related to the first year and personal data.

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Identifying Important Citations Using Contextual Information from Full Text

Cited by 44 publications

References 6 publications

A bibliometric analysis of publications in computer networking research

A bibliometric analysis of publications in computer networking research

Mining the Context of Citations in Scientific Publications

Students Drop Out Trends: A University Study

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