Employing distributional semantics to organize task-focused vocabulary learning

Haemanth Santhi Ponnusamy, Detmar Meurers


Abstract
How can a learner systematically prepare for reading a book they are interested in? In this paper, we explore how computational linguistic methods such as distributional semantics, morphological clustering, and exercise generation can be combined with graph-based learner models to answer this question both conceptually and in practice. Based on highly structured learner models and concepts from network analysis, the learner is guided to efficiently explore the targeted lexical space. They practice using multi-gap learning activities generated from the book. In sum, the approach combines computational linguistic methods with concepts from network analysis and tutoring systems to support learners in pursuing their individual reading task goals.
Anthology ID:
2021.bea-1.3
Volume:
Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications
Month:
April
Year:
2021
Address:
Online
Venues:
BEA | EACL
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
26–36
Language:
URL:
https://aclanthology.org/2021.bea-1.3
DOI:
Bibkey:
Cite (ACL):
Haemanth Santhi Ponnusamy and Detmar Meurers. 2021. Employing distributional semantics to organize task-focused vocabulary learning. In Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications, pages 26–36, Online. Association for Computational Linguistics.
Cite (Informal):
Employing distributional semantics to organize task-focused vocabulary learning (Santhi Ponnusamy & Meurers, BEA 2021)
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PDF:
https://aclanthology.org/2021.bea-1.3.pdf