Declarative Knowledge Extraction in the AC&NL Tutor (CROSBI ID 695482)
Prilog sa skupa u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Grubišić, Ani ; Stankov, Slavomir ; Žitko, Branko ; Šarić-Grgić, Ines ; Gašpar, Angelina ; Tomaš, Suzana ; Brajković, Emil ; Vasić, Daniel
engleski
Declarative Knowledge Extraction in the AC&NL Tutor
Automatic knowledge acquisition is a rather complex and challenging task. This paper focuses on the description and evaluation of a semi-automatic authoring tool (SAAT) that has been developed as a part of the Adaptive Courseware based on Natural Language AC&NL Tutor project. The SAAT analyzes a natural language text and, as a result of the declarative knowledge extraction process, it generates domain knowledge that is presented in a form of natural language sentences, questions and domain knowledge graphs. Generated domain knowledge presents expert knowledge in the intelligent tutoring system Tutomat. The natural language processing techniques are applied and the tool’s functionalities are thoroughly explained. This tool is, to our knowledge, the only one that enables natural language question and sentence generation of different levels of complexity. Using an unstructured and unprocessed Wikipedia text in computer science, evaluation of domain knowledge extraction algorithm, i.e. the correctness of extraction outcomes and the effectiveness of extraction methods, was performed. The SAAT outputs were compared with the gold standard, manually developed by two experts. The results showed that 68.7% of detected errors referred to the performance of the integrated linguistic resources, such as CoreNLP, Senna, WordNet, whereas 31.3% of errors referred to the proposed extraction algorithms.
Natural language processing ; Knowledge extraction ; Automatic question generation ; Question answering evaluation ; Gold standard evaluation ; Intelligent tutoring systems
Book Series: Lecture Notes in Computer Science, Volume: 12214
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Podaci o prilogu
293-310.
2020.
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objavljeno
10.1007/978-3-030-50788-6_22
Podaci o matičnoj publikaciji
Lecture notes in computer science
Sottilare, Robert A. ; Schwarz, Jessica
Cham: Springer
978-3-030-50787-9
0302-9743
1611-3349
Podaci o skupu
2nd Adaptive Instructional Systems (AIS 2020) ; 22nd International Conference on Human-Computer Interaction (HCII 2020)
predavanje
19.07.2020-24.07.2020
Kopenhagen, Danska