Extending co-citation using sections of research articles

Khan, Arjumand Yar; Shahid, Abdul; Afzal, Muhammad Tanvır

doi:10.3906/elk-1703-260

Cited by 12 publications

(10 citation statements)

References 17 publications

(31 reference statements)

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“…14 To rank the relevance of documents, combining citation location extraction with citation analysis, such as an examination of cocitations, did up to 68 per cent better than using just co-citations and 39 per cent better than utilizing proximity citations and has been widely developed to acquire citation-based information. 15 Experiments with other techniques combining citation location calculations and bibliographic couplings provided better results in terms of document similarities than the gold standard content approach methods, such as keyword matching. 16 Research on using frequencies and citation locations for ranking authors or articles is closest to the present research.…”

Section: Citation Location As a Featurementioning

confidence: 99%

Analysis of In text Citation Patterns in Local Journals for Ranking Scientific Documents

Yaniasih

Budi

2021

DESIDOC Jl. Lib. Info. Technol.

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In-text citations have been put forward as a new way to overcome the bias inherent in bibliographic citation analysis. In-text citation patterns have been used as the basis for citation analysis previously, but all the evidence has come from international journals. However, many countries have more local journals than international journals. This paper uses in-text citation analysis to examine local journals in Indonesia. The paper aims to determine the location-based citation pattern in the text and its effect on the articles’ and authors’ rankings. We collected articles from seven food science journals and then parsed these articles to detect the citations and their locations within the text. Pre-processing included normalizing section names, developing a database, and matching citation identities. The rankings were based on sections and then evaluated using the Spearman rank correlation in the final step. The results revealed that Indonesian journals did not exhibit the same patterns as international journals. There were differences in the section locations of the highest percentages of citations, the distributions of publication years, and the ranking methods. The correlations between sections indicated that the citations in the results and discussion section should be give the highest weight, followed by those in the method section, while the lowest weight should be assigned to citations in the introduction. These results need to be strengthened with further research using more extensive data and fields. Other findings, such as nonstandard and inconsistent citations, made developing an automatic citation detection system for local journals challenging.

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Section: Citation Location As a Featurementioning

confidence: 99%

Analysis of In text Citation Patterns in Local Journals for Ranking Scientific Documents

Yaniasih

Budi

2021

DESIDOC Jl. Lib. Info. Technol.

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“…In these models, researchers exploited the behavior of citation within the text [8,15,19]. Researchers analyzed the context of citations [18], number of repeated citations [28], and citations within the sections [29]. The majority of these approaches have investigated co-citation proximity [8,15], while some of the approaches are based on bibliographic coupling.…”

Section: Bibliographic-based Approachesmentioning

confidence: 99%

Combining metadata and co-citations for recommending related papers

Ahmad¹,

Afzal²

2020

Turk J Elec Eng & Comp Sci

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Identification of relevant documents is performed to keep track of the state-of-the-art methods and relies on research paper recommender systems. The proposed approaches for these systems can be classified into categories like content-based, collaborative filtering-based, and bibliographic information-based approaches. The content-based approaches exploit the full text of articles and provide more promising results than other approaches. However, most content is not freely available because of subscription requirements. Therefore, the scope of content-based approaches is limited. In such scenarios, the best possible alternative could be the exploitation of other openly available resources. Therefore, this research explores the possible use of metadata and bibliographic information to find related articles. The approach incorporates metadata with co-citations to find and rank related articles against a query paper. The similarity score of metadata fields is calculated and combined with co-citations. The proposed approach is evaluated on a newly constructed dataset of 5116 articles. The benchmark ranking against each co-cited document set is established by applying Jensen-Shannon divergence (JSD) and results are evaluated with the state-of-the-art content-based approach in terms of normalized discounted cumulative gain (NDCG). The state-of-the-art content-based approach achieved an NDCG score of 0.86 while the traditional co-citation-based approach scored 0.72. The presented method achieved NDCG scores of 0.73, 0.77, and 0.78 by incorporating the title, co-citation and title, and abstract, respectively, whereas the highest NDCG score of 0.77 was achieved by combining co-citations with metadata. However, better results are achieved by incorporating the title and abstract with NDCG score of 0.81. Therefore, it can be concluded that the proposed approach could be a better alternative in cases where content is unavailable.

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“…This step includes fetching the citation from each section of the research article and then calculating the frequencies of citations with respect to section. To perform this experiment, we considered four sections [15], which are common in almost all research articles. The sections are as follows:…”

Section: Section-wise In-text Citation Identificationmentioning

confidence: 99%

“…This research introduces a novel technique for identifying the important citations [15] of research articles. To perform this experiment, we utilized two benchmark datasets, first dataset is collected by Valenzuela and annotated by two domain experts [12], and another was collected by Faiza et al [13] which was annotated by the actual authors of the papers from Capital University of Science and Technology, Islamabad (CUST).…”

Section: Introductionmentioning

confidence: 99%

Important citation identification by exploiting content and section-wise in-text citation count

Nazir¹,

Asif²,

Ahmad³

et al. 2020

PLoS ONE

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A citation is deemed as a potential parameter to determine linkage between research articles. The parameter has extensively been employed to form multifarious academic aspects like calculating the impact factor of journals, h-Index of researchers, allocate different research grants, find the latest research trends, etc. The current state-of-the-art contends that all citations are not of equal importance. Based on this argument, the current trend in citation classification community categorizes citations into important and non-important reasons. The community has proposed different approaches to extract important citations such as citation count, context-based, metadata, and textual based approaches. The contemporary state-of-the-art in citation classification community ignores significantly potential features that can play a vital role in citation classification. This research presents a novel approach for binary citation classification by exploiting section-wise in-text citation frequencies, similarity score, and overall citation count-based features. The study also introduces machine learning algorithms based novel approach for assigning appropriate weights to the logical sections of research papers. The weights are allocated to the citations with respect to their sections. To perform the classification, we used three classification techniques, Support Vector Machine, Kernel Linear Regression, and Random Forest. The experiment was performed on two annotated benchmark datasets that contain 465 and 311 citation pairs of research articles respectively. The results revealed that the proposed approach attained an improved value of precision (i.e., 0.84 vs 0.72) from contemporary state-of-the-art approach.

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Extending co-citation using sections of research articles

Cited by 12 publications

References 17 publications

Analysis of In text Citation Patterns in Local Journals for Ranking Scientific Documents

Analysis of In text Citation Patterns in Local Journals for Ranking Scientific Documents

Combining metadata and co-citations for recommending related papers

Important citation identification by exploiting content and section-wise in-text citation count

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