Citation Classification for Behavioral Analysis of a Scientific Field

Jurgens, David; Kumar, Srijan; Hoover, Raine; McFarland, Daniel A.; Jurafsky, Dan

doi:10.48550/arxiv.1609.00435

Cited by 5 publications

(4 citation statements)

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“…Incorporating citation context information has been shown to improve classification performance [43], [44].The role of similarity between titles and abstracts has been discussed in measuring the value of citations [18], [19], [31]. Additionally, factors such as clue words [19], [35], keywords [45], and the time characteristics of citations are considered. Moreover, incorporating citation context information along with section-wise citation count features has shown promising results [23].…”

Section: Related Workmentioning

confidence: 99%

Exploiting Contextual Word Embedding for Identification of Important Citations: Incorporating Section-Wise Citation Counts and Metadata Features

Iqbal,

Shahid,

Roman

et al. 2023

IEEE Access

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Finding relevant research papers is a challenging task due to the enormous number of scientific publications published each year. In recent years, the scientific community has delved into the analysis of citations at a deep level, specifically examining the content of the papers, in order to identify more important documents. Citations serve as potential parameters for determining linkages between research articles. They have been extensively used for various academic purposes, such as calculating journal impact factors, determining researchers' h-index, allocating research grants, and identifying the latest research trends. However, it has been argued by researchers that not all citations are equally influential. As a result, alternative techniques have been proposed to identify important citations based on content, metadata, and bibliographic information. Nevertheless, the current state-of-the-art approaches still require further improvement. Additionally, the use of deep learning models and word embedding techniques in this context has not been extensively studied. In this research work, we propose an approach based on two primary modules: 1) Section-wise citation count, and 2) metadata-based analysis of citation intent. Our study also involves conducting several experiments using deep learning models combined with FastText, word2vec, and BERT-based word embeddings to perform citation analysis. These experiments were conducted on two benchmark datasets, and the results were compared with a contemporary study that employed a rich set of content-based features for classification. Our findings indicate that the deep learning CNN model coupled with FastText word embedding achieves the best results in terms of accuracy, precision, and recall. It outperforms the existing state-of-the-art model with a precision score of 0. 97.

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Section: Related Workmentioning

confidence: 99%

Exploiting Contextual Word Embedding for Identification of Important Citations: Incorporating Section-Wise Citation Counts and Metadata Features

Iqbal,

Shahid,

Roman

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…However, most existing works focus on citation recommendation [1]- [5], which aims at recommending citations for a given text. Another category of research works focus on citation classification [6]- [11], aiming to reason author's intention for citing a paper. There are also research works about generating a reading list to users.…”

Section: Introductionmentioning

confidence: 99%

Tell Me How to Survey: Literature Review Made Simple with Automatic Reading Path Generation

Ding

Xiang

et al. 2022

2022 IEEE 38th International Conference on Data Engineering (ICDE)

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“…However, most existing works focus on citation recommendation [19,28,33,34,43], which aims at recommending citations for a given text. Another category of research works focus on citation classification [11,13,21,27,36,42], aiming to reason author's intention for citing a paper. There are also research works about generating a reading list to users.…”

Section: Introductionmentioning

confidence: 99%

Tell Me How to Survey: Literature Review Made Simple with Automatic Reading Path Generation

Ding¹,

Xiang²,

Ou³

et al. 2021

Preprint

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Recent years have witnessed the dramatic growth of paper volumes with plenty of new research papers published every day, especially in the area of computer science. How to glean papers worth reading from the massive literature to do a quick survey or keep up with the latest advancement about a specific research topic has become a challenging task. Existing academic search engines such as Google Scholar return relevant papers by individually calculating the relevance between each paper and query. However, such systems usually omit the prerequisite chains of a research topic and cannot form a meaningful reading path. In this paper, we introduce a new task named Reading Path Generation (RPG) which aims at automatically producing a path of papers to read for a given query. To serve as a research benchmark, we further propose Sur-veyBank, a dataset consisting of large quantities of survey papers in the field of computer science as well as their citation relationships. Each survey paper contains key phrases extracted from its title and multi-level reading lists inferred from its references. Furthermore, we propose a graph-optimization-based approach for reading path generation which takes the relationship between papers into account. Extensive evaluations demonstrate that our approach outperforms other baselines. A Real-time Reading Path Generation (RePaGer) system has been also implemented with our designed model. To the best of our knowledge, we are the first to target this important research problem. Our source code of RePaGer system and SurveyBank dataset can be found here.

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Citation Classification for Behavioral Analysis of a Scientific Field

Cited by 5 publications

References 0 publications

Exploiting Contextual Word Embedding for Identification of Important Citations: Incorporating Section-Wise Citation Counts and Metadata Features

Exploiting Contextual Word Embedding for Identification of Important Citations: Incorporating Section-Wise Citation Counts and Metadata Features

Tell Me How to Survey: Literature Review Made Simple with Automatic Reading Path Generation

Tell Me How to Survey: Literature Review Made Simple with Automatic Reading Path Generation

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