Volume 11, Number 16, October 2021

An Intelligent Question Answering Platform for Graduate Enrollment

  Authors

Mengyuan Zhang, Yuting Wang, Jianxia Chen and Yu Cheng, Hubei University of Technology, China

  Abstract

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.

  Keywords

Question Answering System, Graduate Enrollment, Deep Learning, Sentence Semantic Similarity.