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arXiv 2008.13020cs.DLcs.LG
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基于自然语言处理和机器学习技术的十年引文分析:实证研究综述

A Decade of In-text Citation Analysis based on Natural Language Processing and Machine Learning Techniques: An overview of empirical studies

Information Technology University(信息技术大学);King Abdulaziz University(阿卜杜勒阿齐兹国王大学);King Khalid University(哈立德国王大学);Manchester Metropolitan University(曼彻斯特城市大学);Max Planck Society(马克斯·普朗克学会)

Sehrish Iqbal, Saeed-Ul Hassan, Naif Radi Aljohani, Salem Alelyani, Raheel Nawaz, Lutz Bornmann

摘要:

引文分析是研究评价中最常用的方法之一。我们看到通过文献计量学元数据进行引文分析的显著增长,主要是由于Web of Science、Scopus、Google Scholar、Microsoft Academic和Dimensions等引文数据库的可用性。由于近年来更容易获得全文出版物语料库,信息科学家已经远远超出了传统的文献计量学,利用全文数据处理技术的进步来衡量科学出版物的影响。这导致了引用上下文和内容分析、引用分类、引用情感分析、引用摘要和基于引用的推荐方面的技术发展。本文旨在对这些发展的研究进行评述。它的主要重点是使用自然语言处理和机器学习技术来分析引文的出版物。

英文摘要:

Citation analysis is one of the most frequently used methods in research evaluation. We are seeing significant growth in citation analysis through bibliometric metadata, primarily due to the availability of citation databases such as the Web of Science, Scopus, Google Scholar, Microsoft Academic, and Dimensions. Due to better access to full-text publication corpora in recent years, information scientists have gone far beyond traditional bibliometrics by tapping into advancements in full-text data processing techniques to measure the impact of scientific publications in contextual terms. This has led to technical developments in citation context and content analysis, citation classifications, citation sentiment analysis, citation summarisation, and citation-based recommendation. This article aims to narratively review the studies on these developments. Its primary focus is on publications that have used natural language processing and machine learning techniques to analyse citations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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