Exploiting reference section to classify paper's topics

Sajid, Naseer Ahmed; Ali, Tariq; Afzal, Muhammad; Ahmad, Munir; Qadir, Muhammad Abdul

doi:10.1145/2077489.2077531

Cited by 11 publications

(8 citation statements)

References 8 publications

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“…The system fails to provide recommendations in case of a lack of topic information. Sajid et al [20] have proposed finding the topic information based on the references within a paper.…”

Section: ) Metadata Based Methodsmentioning

confidence: 99%

“…The input to our proposed system includes citation context and section in which in-text citation has been made. Literature has made several attempts [19], [20] to investigate the importance of various features to understand the reason of a citation and thus measure relativity among the research documents. Citation context has proved to be the most effective in finding the reason for citation [4], [13].…”

Section: A Feature Selection For Training the Modelmentioning

confidence: 99%

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Investigating Maps of Science Using Contextual Proximity of Citations Based on Deep Contextualized Word Representation

et al. 2022

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The citation intent extraction and classification has long been studied as it is a good measure of relevancy. Different approaches have classified the citations into different classes; including weak and strong, positive and negative, important and unimportant. Others have gone further from binary classification to multi-classes, including extension, use, background, or comparison. Researchers have utilized various elements of the information, including both meta and contents of the paper. The actual context of any referred article lies within the citation context where a paper is referred. Various attempts have been made to study the citation context to capture the citation intent, but very few have encoded the words to their contextual representations. For automated classification, we need to train deep learning models, which take the citation context as input and provides the reason for citing a paper. Deep neural models work on numeric data, and therefore, we must convert the text information to its numeric representation. Natural languages are much complex than computer languages. Computer languages have a pre-defined fixed syntax where each word has a unique meaning. In contrast, every word in natural language may have a different meaning and may well be understood by understanding the position, previous discussion, and neighboring words. The extra information provides the context of a word within a sentence. We have, therefore, used contextual word representation, which is trained through deep neural networks. Deep models require massive data for generalizing the model, however, the existing state-of-the-art datasets don't provide much information for the training models to get generalized. Therefore, we have developed our own scholarly dataset, Citation Context Dataset with Intent (C2D-I), an extension of the C2D dataset. We used a transformers based model for capturing the contextual representation of words. Our proposed method outperformed the existing benchmark methods with F1 score of 89%.

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“…The system fails to provide recommendations in case of a lack of topic information. Sajid et al [20] have proposed finding the topic information based on the references within a paper.…”

Section: ) Metadata Based Methodsmentioning

confidence: 99%

Section: A Feature Selection For Training the Modelmentioning

confidence: 99%

Investigating Maps of Science Using Contextual Proximity of Citations Based on Deep Contextualized Word Representation

et al. 2022

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“…Subsequently, a brief overview of the metadata-based methods has been presented. The author presented a novel approach [31] by manipulating the references portion of the article for the classification and achieved a reasonable accuracy of 70%. A natural language processing approach was proposed by Yohan et al [32] in which they identified named entities and classified them into their different categories.…”

Section: Metadata-based Approachesmentioning

confidence: 99%

A Novel Metadata Based Multi-Label Document Classification Technique

Sajid¹,

Ahmad²,

Rahman³

et al. 2023

Computer Systems Science and Engineering

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From the beginning, the process of research and its publication is an ever-growing phenomenon and with the emergence of web technologies, its growth rate is overwhelming. On a rough estimate, more than thirty thousand research journals have been issuing around four million papers annually on average. Search engines, indexing services, and digital libraries have been searching for such publications over the web. Nevertheless, getting the most relevant articles against the user requests is yet a fantasy. It is mainly because the articles are not appropriately indexed based on the hierarchies of granular subject classification. To overcome this issue, researchers are striving to investigate new techniques for the classification of the research articles especially, when the complete article text is not available (a case of nonopen access articles). The proposed study aims to investigate the multilabel classification over the available metadata in the best possible way and to assess, "to what extent metadata-based features can perform in contrast to content-based approaches." In this regard, novel techniques for investigating multilabel classification have been proposed, developed, and evaluated on metadata such as the Title and Keywords of the articles. The proposed technique has been assessed for two diverse datasets, namely, from the Journal of universal computer science (J.UCS) and the benchmark dataset comprises of the articles published by the Association for computing machinery (ACM). The proposed technique yields encouraging results in contrast to the state-ofthe-art techniques in the literature.

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“…The outcomes of that study revealed that good results are produced against dense and close-packed graphs. In [16] [17], researchers have employed a reference segment of a research paper to locate the topics of the paper. The study follows an assumption that most of the time, authors cite the papers belonging to the same domain or similar category.…”

Section: Related Workmentioning

confidence: 99%

“…To evaluate the proposed methodology, we have employed the standard evaluation measures, precision-recall, and Fmeasure. The reason for the selection of these evaluation parameters is the frequent reporting of these parameters in literature [16] [18] [27]. VOLUME 11, 2020 This work is licensed under a Creative Commons Attribution 4.0 License.…”

Section: H Evaluationmentioning

confidence: 99%

A Comprehensive Evaluation of Metadata-Based Features to Classify Research Paper’s Topics

et al. 2021

Self Cite

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The existing plethora of document classification techniques exploits different data sources either from the content or metadata of research articles. Various journal publishers like Springer, Elsevier, IEEE, etc., do not provide open access to the content of research articles, whereas metadata is freely available there. Metadata like title, keyword, and abstract can serve as a better alternative to the content in various scenarios. In the current literature, researchers have assessed the role of some of the metadata individually. We believe that the collective contribution of metadata parameters can play a significant role in classifying research papers. This paper presents a comprehensive evaluation of the role of metadata, individually as well as in combinations to achieve the objective of research paper classification. Moreover, we have classified the research articles into ACM hierarchy root categories (e.g. general literature, hardware, software, etc.). In this comprehensive evaluation, we have assessed all the possible combinations of metadata features against different classifiers such as Random Forest, K Nearest Neighbor, and Decision Tree. The results of this research reveal that the title keywords combination outperforms other combinations with an F-measure score of 0.88.

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Exploiting reference section to classify paper's topics

Cited by 11 publications

References 8 publications

Investigating Maps of Science Using Contextual Proximity of Citations Based on Deep Contextualized Word Representation

Investigating Maps of Science Using Contextual Proximity of Citations Based on Deep Contextualized Word Representation

A Novel Metadata Based Multi-Label Document Classification Technique

A Comprehensive Evaluation of Metadata-Based Features to Classify Research Paper’s Topics

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