基于深度学习的文本中细粒度知识元抽取方法研究

Li Yu, Li Qian*, Changlei Fu, Huaming Zhao

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

8 引用 (Scopus)

摘要

[Objective] This paper tries to extract fine-grained knowledge units from texts with a deep learning model based on the modified bootstrapping method. [Methods] First, we built the lexicon for each type of knowledge unit with the help of search engine and keywords from Elsevier. Second, we created a large annotated corpus based on the bootstrapping method. Third, we controlled the quality of annotation with the estimation models of patterns and knowledge units. Finally, we trained the proposed LSTM-CRF model with the annotated corpus, and extracted new knowledge units from texts. [Results] We retrieved four types of knowledge units (study scope, research method, experimental data, as well as evaluation criteria and their values) from 17,756 ACL papers. The average precision was 91%, which was calculated manually. [Limitations] The parameters of models were pre-defined and modified by human. More research is needed to evaluate the performance of this method with texts from other domains. [Conclusions] The proposed model effectively addresses the issue of semantic drifting. It could extract knowledge units precisely, which is an effective solution for the big data acquisition process of intelligence analysis.

投稿的翻译标题Extracting Fine-grained Knowledge Units from Texts with Deep Learning
源语言繁体中文
页(从-至)38-45
页数8
期刊Data Analysis and Knowledge Discovery
3
1
DOI
出版状态已出版 - 1月 2019
已对外发布

关键词

  • Bootstrapping
  • Deep Learning
  • Knowledge Unit Extraction
  • LSTM-CRF
  • Named Entity Recognition

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