Identifying Citation Contexts: a Review of Strategies and Goals.
Authors: Agata Rotondi, Angelo Di Iorio, Freddy Limpens (Università di Bologna, 2018)
Topic: Lexical and Semantic Resources and Analysis

Abstract
The Citation Contexts of a cited English. The Citation Contexts of a cited entity can be seen as little tesserae that, ﬁt together, can be exploited to follow the opinion of the scientiﬁc community towards that entity as well as to summarize its most important contents. This mosaic is an excellent resource of information also for identifying topic speciﬁc synonyms, indexing terms and citers’ motivations, i.e. the reasons why authors cite other works. Is a paper cited for comparison, as a source of data or just for additional info? What is the polarity of a citation? Different reasons for citing reveal also different weights of the citations and different impacts of the cited authors that go beyond the mere citation count metrics. Identifying the appropriate Citation Context is the ﬁrst step toward a multitude of possible analysis and researches. So far, Citation Context have been deﬁned in several ways in literature, related to different purposes, domains and applications. In this paper we present different dimensions of Citation Context investigated by researchers through the years in order to provide an introductory review of the topic to anyone approaching this subject.

Introduction
Researchers consider as Citation Context (CC) different snippets of text around a citation marker. These differences of width inﬂuence the applications that exploit CC as source of information. For example, Qazvinian and Radev (2010) showed that using also implicit citations (i.e. sentences that contain information about a speciﬁc secondary source but do not explicitly cite it) for generating surveys, rather than citing sentences alone, improve the results. Ritchie et al. (2008) compared different widths of CC in order to ﬁnd the most appropriate window for identifying Index Terms. They proved that varying the context from which the Index Terms are gathered has a signiﬁcant effect on retrieval effectiveness. Aljaber et al. (2010) tested different sizes of CC for a document clustering experiment. They claimed that a window size of 50 words from either side of the citation marker works better than taking 10 or 30 terms or the citing sentence alone, whatever its size is. From their analysis, relevant synonymous and related vocabulary extracted from this window of text, in combination with an original full-text representation of the cited document, are effective for document clustering. We can claim that the issue of ﬁnding the optimal CC for a speciﬁc application is a challenging task that interests researchers and which is at the base of every study that exploits the CC as a source of information. 1 With the purpose of providing a useful background to anyone approaching this question, in the following sections we give an overview of different dimensions of textual CC investigated in literature. We classiﬁed them in 3 main categories: a) ﬁxed number of characters b) citing sentence c) extended context (ﬁxed and adaptive), and we summarized our analysis in Figure1. We focus on the strategies to identify the correct textual CC of a citation, nevertheless other CC related topics have been investigated in literature as for example citation recommendations (see Farber (2018) and Ebesu (2017)) The belief of the need of a clear introductory survey about how CC has been differently shaped in literature came to our mind when we faced the problem of deﬁning the optimal CC for the Semantic Coloring of Academic References (SCAR) project1 (Di Iorio et al., 2018). The goal of the SCAR project is to enrich bibliographies of scientiﬁc articles by adding explicit meta data about individual bibliographic entries and to characterize these entries according to multiple criteria. With this purpose, we are studying a set of properties to support the automatic characterization of bibliographic entries and one of our primary source of information is the textual content around citation markers, i.e. the CC. We are currently investigating on ﬁnding the best span of text for our needs. By reviewing the literature, we realized that different approaches correspond to different tasks and are also related to the linguistic domain of application. The SCAR project as well as this review are focused on the English language but it would be interesting to extend this study to other languages.

Conclusion
We have reviewed what we consider the most interesting works about CC identiﬁcation in order to provide a solid background to anyone interested in the topic and especially to those researchers who are facing the task of identifying the best approach for their studies. We did not compare the different strategies with the purpose of ranking them, but we rather showed that there exists various relations between a methodology and the usage, domain, and language speciﬁcity of its possible applications.