Review of Automatic Citation Classification Based on Machine Learning

[Objective] This paper summarizes the application of natural language processing and machine learning technology in automatic citation classification. [Coverage] We searched “citation classification”, “citation polarity”,“citation function”and“feature selection”with Scopus database, and retrieved a total of 46 representative literature. [Methods] These research was reviewed from the perspectives of citation classification process, tasks and methods. Then, we discussed their future development trends and challenges. [Results] The research of citation classification is shifting from multi-class to binary class. Deep learning model can classify sentiments and functions of citations simultaneously. The challenges facing automatic citation classification include single discipline corpus, controversial definition of citation contexts and unbalanced classification data. [Limitations] This review does not discuss many classification systems in the industry. [Conclusions] We need to develop the evaluation method for re-using scientific research data such as codes, data and corpus, which could help to build open science. Combining citation classification and counts could establish a multi-dimensional evaluation model. Based on the user’s search results, the system could recommend documents supporting or objecting the related research for further reading. © 2021, Chinese Academy of Sciences. All rights reserved.