Automatic identification of cited text spans: a multi-classifier approach over imbalanced dataset

Ma, Shutian; Xu, Jin; Zhang, Chengzhi

doi:10.1007/s11192-018-2754-2

Cited by 15 publications

(8 citation statements)

References 28 publications

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“…Therefore, each base classifier can be regarded as an expert for a special sample in the classification space. Dynamic selection classifiers exhibit a higher accuracy over traditional combined approaches in solving several real-world problems, such as face recognition [ 30 ] and text verification [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

Zhao

Wang

et al. 2021

Entropy

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Imbalance ensemble classification is one of the most essential and practical strategies for improving decision performance in data analysis. There is a growing body of literature about ensemble techniques for imbalance learning in recent years, the various extensions of imbalanced classification methods were established from different points of view. The present study is initiated in an attempt to review the state-of-the-art ensemble classification algorithms for dealing with imbalanced datasets, offering a comprehensive analysis for incorporating the dynamic selection of base classifiers in classification. By conducting 14 existing ensemble algorithms incorporating a dynamic selection on 56 datasets, the experimental results reveal that the classical algorithm with a dynamic selection strategy deliver a practical way to improve the classification performance for both a binary class and multi-class imbalanced datasets. In addition, by combining patch learning with a dynamic selection ensemble classification, a patch-ensemble classification method is designed, which utilizes the misclassified samples to train patch classifiers for increasing the diversity of base classifiers. The experiments’ results indicate that the designed method has a certain potential for the performance of multi-class imbalanced classification.

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Section: Introductionmentioning

confidence: 99%

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

Zhao

Wang

et al. 2021

Entropy

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“…The studies dealing with summary generation have shown that the language models are more beneficial than the baseline models such as MEAD and LexRank. The authors of these studies took into account linguistic patterns to generate summaries that are not just a combination of sentences (Ma et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…System generated summaries are needed because they contain 9 percentage points more related keywords from the original document than human abstractive summaries Ma et al (2018) Computational Linguistics 40 citing and cited papers set Similarity-based features are more suitable for summarisation than position-based features Yasunaga et al (2019) Computational Linguistics…”

Section: Papersmentioning

confidence: 99%

A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies

et al. 2021

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“…Hence, an appealing parallel research direction is the automatic identification of the citation discourse facet (i.e., task 1b). Most related works have adopted binary classifiers combining various latent features trained using Deep NLP techniques (e.g., Davoodi et al, 2018;Ma et al, 2018;Wang et al, 2018). Other approaches have considered simpler word-or sentence-based relevance scores (e.g., Baruah et al, 2018;Li et al, 2018).…”

Section: Contributions To Cl-scisummmentioning

confidence: 99%

Leveraging full-text article exploration for citation analysis

2021

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Scientific articles often include in-text citations quoting from external sources. When the cited source is an article, the citation context can be analyzed by exploring the article full-text. To quickly access the key information, researchers are often interested in identifying the sections of the cited article that are most pertinent to the text surrounding the citation in the citing article. This paper first performs a data-driven analysis of the correlation between the textual content of the sections of the cited article and the text snippet where the citation is placed. The results of the correlation analysis show that the title and abstract of the cited article are likely to include content highly similar to the citing snippet. However, the subsequent sections of the paper often include cited text snippets as well. Hence, there is a need to understand the extent to which an exploration of the full-text of the cited article would be beneficial to gain insights into the citing snippet, considering also the fact that the full-text access could be restricted. To this end, we then propose a classification approach to automatically predicting whether the cited snippets in the full-text of the paper contain a significant amount of new content beyond abstract and title. The proposed approach could support researchers in leveraging full-text article exploration for citation analysis. The experiments conducted on real scientific articles show promising results: the classifier has a 90% chance to correctly distinguish between the full-text exploration and only title and abstract cases.

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Automatic identification of cited text spans: a multi-classifier approach over imbalanced dataset

Cited by 15 publications

References 28 publications

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies

Leveraging full-text article exploration for citation analysis

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