文章基本信息

标题：An Intelligent Question Answering Platform for Graduate Enrollment
本地全文：下载
作者：Mengyuan Zhang ; Yuting Wang ; Jianxia Chen 等
期刊名称：Computer Science & Information Technology
电子版ISSN：2231-5403
出版年度：2021
卷号：11
期号：16
语种：English
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：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.
关键词：Question Answering System;Graduate Enrollment;Deep Learning;Sentence Semantic Similarity