Learning grammatical categories using paradigmatic representations: Substitute words for language acquisition

Mehmet Ali Yatbaz, Volkan Cirik, Aylin Küntay, Deniz Yuret


Abstract
Learning syntactic categories is a fundamental task in language acquisition. Previous studies show that co-occurrence patterns of preceding and following words are essential to group words into categories. However, the neighboring words, or frames, are rarely repeated exactly in the data. This creates data sparsity and hampers learning for frame based models. In this work, we propose a paradigmatic representation of word context which uses probable substitutes instead of frames. Our experiments on child-directed speech show that models based on probable substitutes learn more accurate categories with fewer examples compared to models based on frames.
Anthology ID:
C16-1068
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
707–716
Language:
URL:
https://www.aclweb.org/anthology/C16-1068
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/C16-1068.pdf