摘要
破除“SCI至上”,从学术全文本视角进行学术研究成果的创新点和贡献点自动识别,对于支持和完善同行评价和代表作评价有显著的数据支撑作用。本文提出一个深度学习与规则结合的学术创新贡献识别方法,首先标注学术文本中潜在创新贡献短语,其次构建以BERT为基础的自动识别模型,同时制定出细粒度的抽取规则,最后应用到大规模数据集的抽取当中。本文选取菊花领域的学术文本进行实证研究,成功由学术文本中自动识别并抽取出可以表征学术成果价值的学术创新点和贡献点。将多个学者的创新贡献用图谱等可视化方法进行综合展示可以成为后续研究的方向。
In line with recent efforts to dismantle the“SCI supremacy”phenomenon,automatic identification of the innovation and contribution of academic research could greatly improve and support both peer review and evaluation of representative work.This study proposes a method for recognizing academic innovation and contribution based on a combination of deep learning and rules.First,we identify the potential phrases related to innovation and scientific contribution in academic texts.Second,we build an automatic recognition model based on BERT,develop fine-grained extraction rules,and apply them to extraction of large-scale data sets.To empirically test the method,we applied it to evaluate research texts in the field of chrysanthemums,and were able to successfully identify and extract potential academic innovation and contributions.Future study could expand this method to visualize innovation and contribution by specific scholars.
作者
周海晨
郑德俊
郦天宇
Zhou Haichen;Zheng Dejun;Li Tianyu(College of Information Science&Technology,Nanjing Agricultural University,Nanjing 210095)
出处
《情报学报》
CSSCI
CSCD
北大核心
2020年第8期845-851,共7页
Journal of the China Society for Scientific and Technical Information
关键词
学术文本
学术创新
学术贡献
自动识别
深度学习
academic text
academic innovation
academic contribution
automatic recognition
deep learning
作者简介
周海晨,男,1993年生,博士研究生,主要研究领域为文本挖掘、知识抽取;郑德俊,男,1968年生,博士,教授,博士生导师,主要研究领域为信息计量分析与科研评价、知识服务与质量控制,E-mail:zdejun@njau.edu.cn;郦天宇,男,1996年生,硕士研究生,主要研究领域为文本挖掘、知识抽取。
