Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (8): 132-144    DOI: 10.11925/infotech.2096-3467.2020.1221
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Extracting Knowledge Elements of Sci-Tech Literature Based on Artificial and Machine Features
Chai Qingfeng1,2,Shi Linyan2,Mei Shan2,Xiong Haitao2,He Huixin1()
1College of Computer Science and Technology, Huaqiao University, Quanzhou 361021, China
2Tongfang Knowledge Network Technology Co., Ltd. (Beijing), Beijing 100192, China
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Abstract

[Objective] This paper merged the artificial and machine features of scientific and technological literature with the help of deep learning method, aiming to improve the efficiency of knowledge element extraction. [Methods] We constructed 26 artificial features based on the characteristics of these literature, which mainly included texts, sentences and words. Then, we combinted these features with Word2Vec, one-hot and other machine features using LSTM, CNN and BERT models and extracted knowledge elements. [Results] The accuracy of feature vertical merging for knowledge element extraction reached 0.91, which was 6 percentage points higher than the performance of most traditional methods. [Limitations] The deep learning model needs to be optimized to process larger amount of data. [Conclusions] The proposed method could effectively improve the results of knowledge element extraction.

Received: 06 December 2020      Published: 15 September 2021
 ZTFLH: G250
Fund:National Social Science Fund of China(19BXW110)
Corresponding Authors: He Huixin ORCID： 0000-0002-1764-6727     E-mail: huixinhe@qq.com