Name
Title | ||
|---|---|---|
An Intelligent Question Answering System for University Courses Based on BiLSTM and Keywords Similarity. |
Abstract | ||
|---|---|---|
The application of intelligent question answering system in college assistant teaching is an effective way to reduce the workload of university teachers and improve students’ learning efficiency. With the rapid development of related technologies, the intelligent question answering system has made great progress, but there is little related work in answering students’ university course questions, and there are some problems such as poor accuracy and non universality. Because of this reason, it cannot fully meet the demands of universities. Therefore, this paper proposes an intelligent question answering system for professional questions. First, we select candidate question-and-answer pairs in the knowledge base through professional word matching, and then use the attention mechanism proposed in this paper and bi-directional long short term memory network (BiLSTM) to calculate the semantic similarity between query questions and candidate questions. Multiplying the semantic similarity by the keywords similarity of the two questions as the final similarity. Finally, we push the three most similar candidate questions and the corresponding answers to students. The experimental results show that the system improves the accuracy of answering students’ university course questions, and is applicable to any university course. |
Year | Venue | Field |
|---|---|---|
2018 | BICS | Semantic similarity,Question answering,Information retrieval,Computer science,Workload,Long short term memory,Knowledge base,Universality (philosophy) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chunyan Ma | 1 | 34 | 3.65 |
| Baomin Li | 2 | 0 | 0.34 |
| Tong Zhao | 3 | 14 | 7.30 |
| Wei Wei | 4 | 480 | 69.55 |