Pregled bibliografske jedinice broj: 1087597
Declarative Knowledge Extraction in the AC&NL Tutor
Declarative Knowledge Extraction in the AC&NL Tutor // Adaptive Instructional Systems: 2nd International Conference, AIS 2020, held as part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020: Proceedings / Sottilare, Robert A. ; Schwarz, Jessica (ur.).
Cham: Springer, 2020. str. 293-310 doi:10.1007/978-3-030-50788-6_22 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1087597 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Declarative Knowledge Extraction in the AC&NL
Tutor
Autori
Grubišić, Ani ; Stankov, Slavomir ; Žitko, Branko ; Šarić-Grgić, Ines ; Gašpar, Angelina ; Tomaš, Suzana ; Brajković, Emil ; Vasić, Daniel
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Adaptive Instructional Systems: 2nd International Conference, AIS 2020, held as part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020: Proceedings
/ Sottilare, Robert A. ; Schwarz, Jessica - Cham : Springer, 2020, 293-310
ISBN
978-3-030-50787-9
Skup
2nd Adaptive Instructional Systems (AIS 2020) ; 22nd International Conference on Human-Computer Interaction (HCII 2020)
Mjesto i datum
Kopenhagen, Danska, 19.07.2020. - 24.07.2020
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Natural language processing ; Knowledge extraction ; Automatic question generation ; Question answering evaluation ; Gold standard evaluation ; Intelligent tutoring systems
Sažetak
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.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
Napomena
Book Series: Lecture Notes in Computer Science,
Volume: 12214
POVEZANOST RADA
Ustanove:
Prirodoslovno-matematički fakultet, Split,
Filozofski fakultet u Splitu
Profili:
Slavomir Stankov
(autor)
Suzana Tomaš
(autor)
Angelina Gašpar
(autor)
Branko Žitko
(autor)
Ani Grubišić
(autor)
Ines Šarić-Grgić
(autor)
Citiraj ovu publikaciju:
Časopis indeksira:
- Scopus