Toward the Automatic Correction of Short Answer Questions

Autor:	Mervat Gheith, Zeinab E. Attia, Waleed Arafa
Rok vydání:	2019
Předmět:	business.industry Computer science Short answer Artificial intelligence computer.software_genre business computer Natural language processing Task (project management)
Zdroj:	Advances in Intelligent Systems and Computing ISBN: 9783030311285 AISI
DOI:	10.1007/978-3-030-31129-2_42
Popis:	The manual correction for short answer questions is a tedious and time consuming task. This issue leads to the importance of having automatic correction systems for short answer questions. Thus this paper proposes three different approaches for correcting short answer questions. One of them is NLP-based while the others are machine learning based approaches. The three approaches are tested and compared across another two short answer correction systems that are evaluated on the same dataset. Results show that the proposed approaches outperform others that are evaluated on the same dataset.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::a7b0215bab4f19f9e4b2e52d6fa526d9 https://doi.org/10.1007/978-3-030-31129-2_42 Zobrazit plný text záznamu