摘要
【目的】在总结当前引文元数据抽取方法的基础上,结合语义学知识和机器学习方法,对引文元数据的自动抽取方法进行探索。【方法】实验中采用神经网络模型对人工分割过的语料进行词向量训练。利用相同类型的元数据会相对集中地出现在向量空间中某一位置的现象,通过支持向量机分类算法实现对元数据的自动归类和标注。【结果】在以外文引文数据作为测试集的实验中,本文方法取得了较高的准确率和召回率,特别是针对引文中含有多种语言和缩写的现象,具有较好的处理能力。【局限】在对于引文元数据时间内容的细粒度抽取中存在一定的局限性。【结论】实验结果表明,此方法在引文元数据的自动发现和标注上具有良好的效果,并能很大程度地提高方法的适用性和容错率。
[Objective] This paper proposes a new method to automatically extract bibliographic metadata, with the help of semantic knowledge and machine learning technologies. [Methods] We used the neural network model to create word vectors from manually split data, and then found that same type of metadata is relatively concentrated at certain locations in the vector space. Thus, we proposed a new SVM classification algorithm to classify and annotate the bibliographic metadata automatically. [Results] The proposed method achieved high recall and precision rates with citation data, especially for citations with various languages and abbreviations. [Limitations] The fine-grained extraction of the time related content could be improved. [Conclusions] The proposed method could effectively detect and tag bibliographic metadata, and improve the system's compatibility and fault tolerance ability.
出处
《数据分析与知识发现》
CSSCI
CSCD
2017年第1期47-54,共8页
Data Analysis and Knowledge Discovery
关键词
引文元数据
元数据抽取
机器学习
神经网络
Bibliographic Metadata
Metadata Extraction
Machine Learning
Neural Network