Neural Multi-task Learning for Citation Function and Provenance

Su, Xuan; Prasad, Animesh; Kan, Min-Yen; Sugiyama, Kazunari

doi:10.1109/jcdl.2019.00122

Cited by 21 publications

(15 citation statements)

References 12 publications

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“…A major short-coming of the automatic citation classification based on machine learning methods is its requirement for manual determination of the features prior to training the model (Su et al, 2019). The success of such models rely on how well these features capture the syntactic as well as the semantic information from the citation context.…”

Section: Traditional Machine Learning-based Methodsmentioning

confidence: 99%

“…Alternatively, the developers of the citation schema (Agarwal et al, 2010;Teufel et al, 2006a) or the cited authors themselves annotated the citations (Nazir et al, 2020b;Pride et al, 2019;Zhu et al, 2015). Recently, crowdsourcing platforms are also been utilized for tagging citation labels Munkhdalai et al, 2016;Pride et al, 2019;Su et al, 2019).…”

Section: Annotation Guidelinesmentioning

confidence: 99%

“…Authors may use citations to substantiate or refute their claims. The citation context, which contains the pointer to the referenced article reflects the information about the cited paper (Su et al, 2019). JHA et al, 2017) defined explicit citing sentences as the "...sentences in which actual citations appear...".…”

Section: Citation Context Detectionmentioning

confidence: 99%

“…Often this could be challenging as there are considerable variations in the amount of text surrounding the citations that talk about the cited paper. Rotondi et al (2018) mention the following possibilities for citation context window size: (1) Fixed number of characters -use of 200 characters by ParsCit 20 , (Jurgens et al, 2018), (2) Citing sentence - (Bertin et al, 2016;Garzone and Mercer, 2000;Hassan et al, 2018;Pride et al, 2019;Sula and Miller, 2014;Valenzuela et al, 2015) and (3) Extended context -three or more sentences including the sentences immediately preceding and following the citing sentence (fixed context) Agarwal et al, 2010;Athar and Teufel, 2012a;HERNÁNDEZ-ALVAREZ et al, 2017;Munkhdalai et al, 2016;Nanba et al, 2000;Su et al, 2019;Teufel et al, 2006a) and using all mentions of citations in the article (adaptive context) (Athar and Teufel, 2012b).…”

Section: Citation Context Detectionmentioning

confidence: 99%

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A meta-analysis of semantic classification of citations

Kunnath

Herrmannova

Pride

et al. 2021

Quantitative Science Studies

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The aim of this literature review is to examine the current state-of-the-art in the area of citation classification. In particular, we investigate the approaches for characterising citations based on their semantic type. We conduct this literature review as a metaanalysis covering 60 scholarly articles in this domain. Although we included some of the manual pioneering works in this review, more emphasis is given on the later automated methods, which use Machine Learning and Natural Language Processing (NLP) for analysing the fine-grained linguistic features in the surrounding text of citations. The sections are organised based on the steps involved in the pipeline for citation classification. Specifically, we explore the existing classification schemes, datasets, pre-processing methods, the extraction of contextual and non-contextual features and the different types of classifiers and evaluation approaches. The review highlights the importance of identifying the citation types for research evaluation, the challenges faced by the researchers in the process and the existing research gaps in this field. Peer Review https://publons.com/publon/10.1162/qss_a_00159

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Section: Traditional Machine Learning-based Methodsmentioning

confidence: 99%

Section: Annotation Guidelinesmentioning

confidence: 99%

Section: Citation Context Detectionmentioning

confidence: 99%

Section: Citation Context Detectionmentioning

confidence: 99%

See 2 more Smart Citations

A meta-analysis of semantic classification of citations

Kunnath

Herrmannova

Pride

et al. 2021

Quantitative Science Studies

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“…Representative SDP tasks include parsing embedded reference strings (Prasad et al, 2018;Thai et al, 2020); identifying the importance, sentiment and provenance for citations Su et al, 2019); identifying logical sections and markup (Luong et al, 2012); parsing of equations, figures and tables (Clark and Divvala, 2016); and article summarization (Qazvinian and Radev, 2008 et al., 2013;Cohan and Goharian, 2015;. SDP tasks, in turn, help downstream systems and assist scholars in finding relevant documents and manage their knowledge discovery and utilization workflows.…”

Section: Introductionmentioning

confidence: 99%

SciWING– A Software Toolkit for Scientific Document Processing

Kashyap

Kan

2020

Proceedings of the First Workshop on Scholarly Document Processing

Self Cite

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We introduce SciWING, an open-source software toolkit which provides access to state-ofthe-art pre-trained models for scientific document processing (SDP) tasks, such as citation string parsing, logical structure recovery and citation intent classification. Compared to other toolkits, SciWING follows a full neural pipeline and provides a Python interface for SDP. When needed, SciWING provides fine-grained control for rapid experimentation with different models by swapping and stacking different modules. Transfer learning from general and scientific documents specific pretrained transformers (i.e., BERT, SciBERT, etc.) can be performed. SciWING incorporates ready-to-use web and terminal-based applications and demonstrations to aid adoption and development. The toolkit is available from http://sciwing.io and the demos are available at http://rebrand. ly/sciwing-demo 1 .

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Extracting the evolutionary backbone of scientific domains: The semantic main path network analysis approach based on citation context analysis

Jiang

Liu

2023

Asso for Info Science & Tech

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Main path analysis is a popular method for extracting the scientific backbone from the citation network of a research domain. Existing approaches ignored the semantic relationships between the citing and cited publications, resulting in several adverse issues, in terms of coherence of main paths and coverage of significant studies. This paper advocated the semantic main path network analysis approach to alleviate these issues based on citation function analysis.A wide variety of SciBERT-based deep learning models were designed for identifying citation functions. Semantic citation networks were built by either including important citations, for example, extension, motivation, usage and similarity, or excluding incidental citations like background and future work.Semantic main path network was built by merging the top-K main paths extracted from various time slices of semantic citation network. In addition, a three-way framework was proposed for the quantitative evaluation of main path analysis results. Both qualitative and quantitative analysis on three research areas of computational linguistics demonstrated that, compared to semantics-agnostic counterparts, different types of semantic main path networks provide complementary views of scientific knowledge flows. Combining them together, we obtained a more precise and comprehensive picture of domain evolution and uncover more coherent development pathways between scientific ideas.

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Neural Multi-task Learning for Citation Function and Provenance

Cited by 21 publications

References 12 publications

A meta-analysis of semantic classification of citations

A meta-analysis of semantic classification of citations

SciWING– A Software Toolkit for Scientific Document Processing

Extracting the evolutionary backbone of scientific domains: The semantic main path network analysis approach based on citation context analysis

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