Linguistically-Driven Strategy for Concept Prerequisites Learning on Italian
| Autor: | Chiara Alzetta, Alessio Miaschi, Felice Dell'Orletta, Franco Alberto Cardillo |
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| Rok vydání: | 2019 |
| Předmět: |
Training set
Exploit Computer science business.industry Learning object 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences 020204 information systems Educational resources 0202 electrical engineering electronic engineering information engineering Learning methods Artificial intelligence business Classifier (UML) computer Natural language processing 0105 earth and related environmental sciences |
| Zdroj: | BEA@ACL Scopus-Elsevier |
| Popis: | We present a new concept prerequisite learning method for Learning Object (LO) ordering that exploits only linguistic features extracted from textual educational resources. The method was tested in a cross- and in- domain scenario both for Italian and English. Additionally, we performed experiments based on a incremental training strategy to study the impact of the training set size on the classifier performances. The paper also introduces ITA-PREREQ, to the best of our knowledge the first Italian dataset annotated with prerequisite relations between pairs of educational concepts, and describe the automatic strategy devised to build it. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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