Related references
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Article
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Summary: This article presents a deep learning-based citation context classification architecture using a large annotated dataset. The proposed model outperforms existing feature-based citation classification models, achieving better performance in both binary and multi-class citation classification tasks. The use of focal-loss and class-weight functions helps overcome the issue of class imbalance in the dataset.
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Summary: Traditional citation analyses have limitations in using quantitative methods only. This article proposes a deep learning model to classify citation meanings automatically, including sentiment, role, and function. The proposed model is compared with classic models and shows good performance in classifying citation meanings. The study also reveals similar patterns of citation meaning across different fields of science. The automatic classification metric achieves high scores, especially for unbalanced datasets. The ability to classify citation meanings automatically is important for analyzing big data of journal citations.
Article
Computer Science, Information Systems
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Summary: This study proposes a new metric and method to map and track the main topics and research interests in a knowledge domain through citations of the same dataset. The study utilizes co-word network modularity analysis and topic modeling for analysis, and discusses the methodological implications of using data citation for delineating a knowledge domain and mapping.
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Summary: This paper proposes a semantic main path network analysis approach to address the issues of coherence and coverage in main path analysis by considering the semantic relationships between citing and cited publications. It builds semantic citation networks by including important citations and builds a semantic main path network by merging top-K main paths. The results show that semantic main path networks provide complementary views of scientific knowledge flows and uncover more coherent development pathways.
JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY
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Computer Science, Artificial Intelligence
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Summary: The fast development of science and technology has led to an increase in cutting edge research. However, researchers face the challenge of keeping up with the growing number of publications, especially with the rise of preprint publishing. To address this, a practical framework called Master Reading Tree (MRT) is proposed to trace the evolution of scientific publications. MRT allows researchers to generate annotated evolution roadmaps and identify important previous works or evolution tracks, providing a comprehensive understanding of the field.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
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Article
Computer Science, Interdisciplinary Applications
Yang Zhang et al.
Summary: This article discusses the importance of citations in scientific papers and the value of incorporating rich native information features for citation function classification. It introduces a new dataset called NI-Cite, which includes labeled citations with five key native features, and proposes the use of text representation models integrated with this information to evaluate classification performance.
Review
Computer Science, Interdisciplinary Applications
Yuzhuo Wang et al.
Summary: This article provides a systematic review of the extraction of method entities from academic literature and explores the use of these entities in building knowledge services. By defining and reviewing various approaches for extracting and evaluating method entities, as well as examining the construction of new applications, this study offers insights for researchers in understanding and selecting appropriate research methods.
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Tirthankar Ghosal et al.
Summary: Finding the lineage of a research topic is crucial for advancing scientific displacement. This paper discusses the investigation of discovering significant citations and leveraging them to build a research lineage. Two real-life case studies demonstrate the effectiveness of the proposed method.
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Suchetha N. Kunnath et al.
Summary: This literature review examines the current state of the art in citation classification and the approaches used to characterize citations based on their semantic type. The review emphasizes the use of machine learning and natural language processing to analyze linguistic features in the surrounding text of citations. It highlights the importance of identifying citation types for research evaluation, the challenges researchers face in the process, and existing research gaps in the field.
QUANTITATIVE SCIENCE STUDIES
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Summary: This paper presents a novel approach to evaluate the impact of learner diversity on the generation of heterogeneous ensembles, conducting an exhaustive study using 27 different multiclass datasets and analyzing their results in detail. The presence of labelling noise is also taken into consideration to determine the performance of the different results.
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Article
Computer Science, Interdisciplinary Applications
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Summary: Citation analysis is a prevalent method in the field of information science, and this study used a meta-synthesis approach to establish a new comprehensive classification of citation motivations. The classification includes 35 descriptive concepts and 13 analytic themes, with scientific motivations and tactical motivations as the two main categories of citing reasons, serving rhetorical and social/benefit-oriented functions respectively. This synthesis contributes to bibliometric and scientific evaluation theory by providing a unified annotation schema for citation classification.
Article
Computer Science, Interdisciplinary Applications
Naif Radi Aljohani et al.
Summary: Citations in scholarly documents do not always have equivalent functions or importance. By using machine learning models and feature engineering, researchers were able to classify and predict the importance of citations in academic literature. The Random Forest model showed superior performance in predicting citation importance compared to other models.
Article
Computer Science, Information Systems
Anqing Zheng et al.
Summary: This study focuses on utilizing citation information in scientific literature to extract relations between online resources and scientific terms, aiming to improve online scientific resource profiling. Experimental results show that our framework outperforms other methods by around 5% in scientific information extraction tasks, indicating a promising step towards utilizing citation information to enhance online scientific resource profiling.
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(2021)
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Sehrish Iqbal et al.
Summary: This article discusses the importance of in-text citation analysis in research evaluation and how advancements in full-text data processing techniques have been used to measure the impact of scientific publications. The focus of the research is on publications that have used natural language processing and machine learning techniques to analyze citations.
Article
Information Science & Library Science
Josh M. Nicholson et al.
Summary: Citation indices, while useful for research evaluation, lack context information which can lead to issues when using citations in research assessment. To address this, scite, an intelligent citation index has been developed to categorize citations based on context, providing insight into how they were used.
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(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Roger Ferrod et al.
Summary: Searching for relevant information in large scholarly databases is becoming challenging, but deeper semantic representations of citations could be beneficial; CiTelling is a new model that can express the fine-grained semantic structures behind citation sentences, and through extensive annotation, its validity and reliability have been tested.
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Muhammad Roman et al.
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