A Semantics-based Model for Predicting Children's Vocabulary
Name
IJCAI_camera_ready-1.pdf
Description
Accepted version
Size
619.35 KB
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Unknown
Checksum (MD5)
797593d5dce96bd070fdee892c3bfe03
Author(s)
Grover, Ishaan
Park, Hae Won
Breazeal, Cynthia Lynn
Date Issued
2019
Journal
IJCAI International Joint Conference on Artificial Intelligence
Publisher
International Joint Conferences on Artificial Intelligence
Citation
Grover, Ishaan, Park, Hae Won and Breazeal, Cynthia. 2019. "A Semantics-based Model for Predicting Children's Vocabulary." IJCAI International Joint Conference on Artificial Intelligence, 2019-August.
Version
Author's final manuscript
Abstract
© 2019 International Joint Conferences on Artificial Intelligence. All rights reserved. Intelligent tutoring systems (ITS) provide educational benefits through one-on-one tutoring by assessing children's existing knowledge and providing tailored educational content. In the domain of language acquisition, several studies have shown that children often learn new words by forming semantic relationships with words they already know. In this paper, we present a model that uses word semantics (semantics-based model) to make inferences about a child's vocabulary from partial information about their existing vocabulary knowledge. We show that the proposed semantics-based model outperforms models that do not use word semantics (semantics-free models) on average. A subject-level analysis of results reveals that different models perform well for different children, thus motivating the need to combine predictions. To this end, we use two methods to combine predictions from semantics-based and semantics-free models and show that these methods yield better predictions of a child's vocabulary knowledge. Our results motivate the use of semantics-based models to assess children's vocabulary knowledge and build ITS that maximizes children's semantic understanding of words.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.24963/IJCAI.2019/188
