Keywords in academic papers play an important role in revealing the themes of papers,improving the accuracy of literature retrieval and promoting academic communication.To address the problem of non-standard keyword selection in academic papers,the abstracts of some Chinese academic papers in the field of computer sci-ence were collected by web crawlers,and the mapping data between the abstracts of academic papers and the standardized terms were annotated based on the terminology bank approved by China National Committee for Ter-minology in Science and Technology.Using this dataset and deep learning technology,a matching model between the abstracts of academic papers and the standardized terms is established to realize computer-aided keyword extraction of academic papers.The feasibility of the proposed method was verified through experiments.Addition-ally,through experience replay-based incremental training and evaluation of the data,the incremental generali-zation capability of the model is experimentally verified.
关键词
深度学习/术语库/学术论文/关键词抽取
Key words
deep learning/terminology bank/academic papers/keyword extraction
维普
万方数据