Frontiers in Educational Psychology, 2024, 5(1); doi: 10.38007/JEP.2024.050108.
Kang Wang
The College of Information Engineering, Wuchang Institute of Technology, Wuhan, Hubei, China
In this paper, we investigate an intelligent question-answering system based on knowledge graphs. We first discuss the construction of knowledge graphs, the entity recognition scheme, and a novel question parsing algorithm models. Specifically, we employ the BiLSTM-CRF model for named entity recognition, which integrates Bidirectional Long Short-Term Memory Networks (BiLSTM) and Conditional Random Fields (CRF) to significantly enhance recognition accuracy and efficiently capture the contextual information. For question parsing, We use the BERT pre-trained language model, which can achieve an exact matching between questions and templates based on the high-dimensional vector encoding. In order to enhance the system performance and user experience, we introduce the Model-View-Controller (MVC) scheme to optimize our proposed system architecture. In particular, the data layer is responsible for data access, the logic processing layer handles algorithm and logic processing, and the user interaction layer provides a friendly interface and interaction method. The experiment results show the proposed intelligent question-answering system has made significant progress in entity recognition and question parsing and can achieve efficient operation and user-friendly interaction experience.
Question-Answering System; Knowledge Graph; BERT; BiLSTM-CRF
Kang Wang. Educational Psychology Question Answering System Based on BERT Pre-training Language Model. Frontiers in Educational Psychology (2024), Vol. 5, Issue 1: 62-70. https://doi.org/10.38007/JEP.2024.050108.
[1]Hartmann, W. K. (1975) Lunar "cataclysm": A misconception. Icarus, 24(2), 181-187.
[2]Green, B. F., Alice, K. W., Chomsky, N., et al. (1961) Baseball: an automatic question-answerer. Proceedings of the Western Joint Computer Conference. IEEE Computer Society.
[3]Weizenbaum, J. (1966) ELIZA-Acomputer Program For the Study of Natural Language Communication Between Manand Machine. Communications of the ACM, 9(1), 36-45.
[4]Divyatharshini, G., Devi, P. G., Srimathi, S., et al. (2018) An Efficient Community Question Answer (CQA) using the Metadata Rating. IJARIIT, 2.
[5]Uyar, A., Aliyu, F. M. (2015) Evaluatings earch features of Google Knowledge Graphand Bing Satori. Online In formation Review, 39(2), 197-213.
[6]Bollacker, K. (2008) Freebase: a collaboratively created graph database for structuring human knowledge. Proceedings of the 2008 ACMSIGMOD International Conference on Management of Data, 1247-1250.
[7]Vrandecic, D., Krtoetzsch, M. (2014) Wikidata: a free collaborative knowledgebase. Communications of the ACM, 57(10), 78-85.
[8]Suchanek, F. M., Kasneci, G., Weikum, G. (2007) YAGO: acoreofsemantic knowledge unifying Word Netand Wikipedia. Proceedings of the 16th International Conference on World Wide Web, 697.
[9]Auer, S., AI, E. (2007) DBpedia: anucleus for awebofopen data. Proceedings of the 6th Semantic Web, 722-735.
[10]Richens, R. H. (1958) Interlingual Machine Translation. The Computer Journal, 3, 144-147.
[11]Lenat, D. B. (1989) Building Large Knowledge-Based Systems–Representation and Inference in the CYC Project. Addison-Wesley Pub. Co.
[12]Marti, A. (1998) WordNet: An Electronic Lexical Database. Automated Discovery of Word Net Relations, 5.
[13]Kuck, G. (2004) TimBerners-Lee's Semantic Web. SA Journal of Information Management, 6(1).
[14]Vrandecic, D., Krtoetzsch, M. (2014) Wikidata: a free collaborative knowledgebase. Communications of the ACM, 57(10), 78-85.
[15]Hodges, A. (1984) Alan Turing: The Enigma. Physics Today, 37(11).