Using crowdsourced exercises for vocabulary training to expand ConceptNet
| Autor: | Rodosthenous, Christos, Lyding, Verena, Sangati, Federico, König, Alexander, ul Hassan, Umair, Nicolas, Lionel, Horbačauskienė, Jolita, Katinskaia, Anisia, Aparaschivei, Lavinia |
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| Přispěvatelé: | European Language Resources Association (ELRA), Calzolari, N, Bechet, F, Blache, P, Choukri, K, Cieri, C, Declerck, T, Goggi, S, Isahara, H, Maegaard, B, Mariani, J, Mazo, H, Moreno, A, Odijk, J, Piperidis, S, Department of Computer Science, Department of Digital Humanities, Department of Languages |
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| Zdroj: | Scopus-Elsevier Verena Lyding Eurac Research |
| Popis: | In this work, we report on a crowdsourcing experiment conducted using the V-TREL vocabulary trainer which is accessed via a Telegram chatbot interface to gather knowledge on word relations suitable for expanding ConceptNet. V-TREL is built on top of a generic architecture implementing the implicit crowdsourding paradigm in order to offer vocabulary training exercises generated from the commonsense knowledge-base ConceptNet and - in the background - to collect and evaluate the learners' answers to extend ConceptNet with new words. In the experiment about 90 university students learning English at C1 level, based on the Common European Framework of Reference for Languages (CEFR), trained their vocabulary with V-TREL over a period of 16 calendar days. The experiment allowed to gather more than 12,000 answers from learners on different question types. In this paper, we present in detail the experimental setup and the outcome of the experiment, which indicates the potential of our approach for both crowdsourcing data as well as fostering vocabulary skills. |
| Databáze: | OpenAIRE |
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