Detecting and analyzing missing citations to published scientific entities
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Abstract (arXiv)
Proper citation is of great importance in academic writing for it enables knowledge accumulation and maintains academic integrity. However, citing properly is not an easy task. For published scientific entities, the ever-growing academic publications and over-familiarity of terms easily lead to missing citations. To deal with this situation, we design a special method Citation Recommendation for Published Scientific Entity (CRPSE) based on the cooccurrences between published scientific entities and in-text citations in the same sentences from previous researchers. Experimental outcomes show the effectiveness of our method in recommending the source papers for published scientific entities. We further conduct a statistical analysis on missing citations among papers published in prestigious computer science conferences in 2020. In the 12,278 papers collected, 475 published scientific entities of computer science and mathematics are found to have missing citations. Many entities mentioned without citations are found to be well-accepted research results. On a median basis, the papers proposing these published scientific entities with missing citations were published 8 years ago, which can be considered the time frame for a published scientific entity to develop into a well-accepted concept. For published scientific entities, we appeal for accurate and full citation of their source papers as required by academic standards.
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 19 of its 72 references that are in the map. First posted on arXiv 18 Oct 2022.
Builds on
19 of 72 references.
References · 19
- 2017Attention Is All You Needcited by 60,106
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,304
- 2014Adam: A Method for Stochastic Optimizationcited by 37,093
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,318
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,360
- 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,216
- 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,810
- 2016Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answeringcited by 2,506
- 2015VQA: Visual Question Answeringcited by 2,495
- 2018Deep contextualized word representationscited by 2,286
- 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,159
- 2019SciBERT: A Pretrained Language Model for Scientific Textcited by 1,409
7 more
- 2019SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognitioncited by 1,359
- 2018VoxCeleb2: Deep Speaker Recognitioncited by 864
- 2018Scaling Egocentric Vision: The EPIC-KITCHENS Datasetcited by 792
- 2018AllenNLP: A Deep Semantic Natural Language Processing Platformcited by 347
- 2019ScispaCy: Fast and Robust Models for Biomedical Natural Language Processingcited by 244
- 2019A Context-Aware Citation Recommendation Model with BERT and Graph Convolutional Networkscited by 28
- 2015Homophily and missing links in citation networkscited by 21
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Citing papers per year: 2023: 1.
1 paper.