Journal of Guangxi Normal University(Natural Science Edition) ›› 2021, Vol. 39 ›› Issue (2): 51-61.doi: 10.16088/j.issn.1001-6600.2020060402

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An Automatic Scoring Method for Subjective Questions Using Semantic Technologies and LSTM

XU Qingting1,2, ZHANG Lanfang3*, ZHU Xinhua1,2   

  1. 1. College of Computer Science and Information Engineering,Guangxi Normal University, Guilin Guangxi 541004, China;
    2. Guangxi Collaborative Innovation Center of Multisource Information Integration and Intelligent Processing, GuilinGuangxi 541004, China;
    3. Department of Education, Guangxi Normal University, Guilin Guangxi 541004, China
  • Received:2020-06-04 Revised:2020-10-11 Online:2021-03-25 Published:2021-04-15

Abstract: Aiming at the shortcomings of the existing subjective question marking, this paper proposes an automatic scoring method for subjective questions using semantic technology and LSTM. Firstly, the LSTM neural network classification model is constructed to realize the simultaneous double classification of the title type and question type of the subjective question corpus. It provides the early guarantee for the realization of different question types using different automatic scoring methods; Then, syntax analysis and dependencies analysis are used to analyze questions and answers (standard answers and student’s answers), the components of interrogative words in questions are obtained through the dependencies analysis of title, and the core semantic, the non-core semantic and the negative tone of the answers are obtained through the dependencies analysis of the answers. Finally, for different types of questions, different automatic marking methods of subjective questions are adopted to realize the self-adaptive marking of different students with the same standard answers, which further improves the flexibility and accuracy of the subjective question scoring system.

Key words: LSTM, dependencies, subjective questions, self-adaptive scoring

CLC Number: 

  • TP391
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