Mining the Context of Citations in Scientific Publications

Abstract

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.

The Use of Academic Social Networking Sites in Scholarly Communication: Scoping Review

“…Fewer papers have explicitly mentioned the use of certain theoretical underpinnings. In this regard, altmetric inquiry, human information behavior, scholarly information exchange, use and gratification theory, academic branding, scholarly norms, and networked scholarship are the key concepts mentioned in the reviewed papers (Ali et al, 2017;Borah, 2017;Camilleri, 2017;Gao et al, 2020;González-Solar, 2018;Goodwin et al, 2014;Hassan et al, 2018;Haustein et al, 2015;Herman & Nicholas, 2019;Hong et al, 2013;Jeng et al, 2017;Kiwanuka, 2015;Koranteng & Wiafe, 2018;Manca & Ranieri, 2017a;Hagit Meishar-Tal & Pieterse, 2017;Safder & Hassan, 2019;Thelwall & Kousha, 2014;Veletsianos & Kimmons, 2012;Wang et al, 2019;Wu et al, 2017;Yim & Shin, 2013). The diversity of these theoretical and conceptual orientation reaffirms that ASNs have attracted the interest of scholars from multiple disciplines.…”

A novel focal-loss and class-weight-aware convolutional neural network for the classification of in-text citations

“…Studies that address the classification of citations into important and non-important usually exploit supervised machine learning techniques. By contrast, Hassan et al [21] cluster citations in their dataset into these two groups by using a Self-Organising Map (SOM) to find the qualitative representations of the features and for the better data visualisation. Interestingly, the non-important citation class formulates an independent cluster with neighbouring neurons.…”

Citation-augmented text reuse detection

“…Citation detection in modern scientific contexts is often treated as a solved problem, as in a lot of research using citation networks are used as the starting point for further analysis, like (Asatani et al, 2018), (Eto, 2019), or (Holm et al, 2021), or using the context of citations whose locations are known a priori to characterize the nature of the citation as in (Hassan et al, 2018). In humanistic domains, where the format of citations is more variable, work on the citation locating and parsing remains ongoing, as evidenced by recent work such as (Colavizza et al, 2018) and (Rodrigues Alves et al, 2018) applying deep learning to locate citations in a wider variety of situations in academic writing in the humanities from the 19 th century to today.…”