Open AccessAn Intelligent Question Answering Platform for Graduate Enrollment
Author(s) -
Mengyuan Zhang,
Yuting Wang,
Jianxia Chen,
Yin-Hui Cheng
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5121/csit.2021.111602
Subject(s) - computer science , semantics (computer science) , convolutional neural network , artificial intelligence , quality (philosophy) , question answering , volume (thermodynamics) , graduate students , machine learning , term (time) , logistic regression , information retrieval , natural language processing , medical education , programming language , medicine , philosophy , physics , epistemology , quantum mechanics
To enhance the competitiveness of colleges and universities in the graduate enrollment and reduce the pressure on candidates for examination and consultation, it is necessary and practically significant to develop an intelligent Q&A platform, which can understand and analyze users' semantics and accurately return the information they need. However, there are problems such as the low volume and low quality of the corpus in the graduate enrollment, this paper develops a question answering platform based on a novel retrieval model including density-based logistic regression and the combination of convolutional neural networks and bidirectional long short-term memory. The experimental results show that the proposed model can effectively alleviate the problem of data sparseness and greatly improve the accuracy of the retrieval performance for the graduate enrollment.