WERECE: An Unsupervised Method for Educational Concept Extraction Based on Word Embedding Refinement

• Published in: Applied sciences Vol. 13; no. 22; p. 12307
• Main Authors: Huang, Jingxiu, Ding, Ruofei, Wu, Xiaomin, Chen, Shumin, Zhang, Jiale, Liu, Lixiang, Zheng, Yunxiang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2023
• Subjects: Algorithms; Artificial intelligence; Automation; Big data; clustering; Computational linguistics; concept extraction; Data mining; Deep learning; Dictionaries; Distance learning; Educational evaluation; Information retrieval; Knowledge; knowledge graph; Language processing; Machine learning; manifold learning; Methods; Natural language interfaces; semantic computation; Semantics; Statistical methods; word embedding

The era of educational big data has sparked growing interest in extracting and organizing educational concepts from massive amounts of information. Outcomes are of the utmost importance for artificial intelligence–empowered teaching and learning. Unsupervised educational concept extraction methods b...