Measuring the Evolution of a Scientific Field through Citation Frames

Abstract

Citations have long been used to characterize the state of a scientific field and to identify influential works. However, writers use citations for different purposes, and this varied purpose influences uptake by future scholars. Unfortunately, our understanding of how scholars use and frame citations has been limited to small-scale manual citation analysis of individual papers. We perform the largest behavioral study of citations to date, analyzing how scientific works frame their contributions through different types of citations and how this framing affects the field as a whole. We introduce a new dataset of nearly 2,000 citations annotated for their function, and use it to develop a state-of-the-art classifier and label the papers of an entire field: Natural Language Processing. We then show how differences in framing affect scientific uptake and reveal the evolution of the publication venues and the field as a whole. We demonstrate that authors are sensitive to discourse structure and publication venue when citing, and that how a paper frames its work through citations is predictive of the citation count it will receive. Finally, we use changes in citation framing to show that the field of NLP is undergoing a significant increase in consensus.

Structural Scaffolds for Citation Intent Classification in Scientific Publications

“…We note that these results were obtained without using hand-curated features or additional linguistic resources as used in Jurgens et al (2018). We also experimented with adding features used in Jurgens et al (2018) to our best model and not only we did not see any improvements, but we observed Table 4 shows the main results on SciCite dataset, where we see similar patterns. Each scaffold task improves model performance.…”

“…To address these problems and to unify previous efforts, in a recent work, Jurgens et al (2018) proposed a six category system for citation intents. In this work, we focus on two schemes: (1) the scheme proposed by Jurgens et al (2018) and (2) an additional, more coarse-grained generalpurpose category system that we propose (details in §3). Unlike other schemes that are domainspecific, our scheme is general and naturally fits in scientific discourse in multiple domains.…”

“…Title: Gait asymmetry in patients with Parkinson's disease and elderly fallers ... han and , and studying evolution of scientific fields (Jurgens et al, 2018).…”

“…In this work, we approach the problem of citation intent classification by modeling the language expressed in the citation context. A citation context includes text spans in a citing paper describing a referenced work and has been shown to be the primary signal in intent classification (Teufel et al, 2006;Abu-Jbara et al, 2013;Jurgens et al, 2018). Existing models for this problem are feature-based, modeling the citation context with respect to a set of predefined handengineered features (such as linguistic patterns or cue phrases) and ignoring other signals that could improve prediction.…”

Domain-Independent Extraction of Scientific Concepts from Research Articles

“…More recent corpora have been annotated at phrasal level. SciCite [9] and ACL ARC [21] are datasets for citation intent classification from Computer Science, Medicine, and Computational Linguistics. ACL RD-TEC [18] from Computational Linguistics aims at extracting scientific technology and non-technology terms.…”