Could Machine Learning Shed Light on Natural Language Complexity?

Maria Dolores Jiménez-López, Leonor Becerra-Bonache


Abstract
In this paper, we propose to use a subfield of machine learning –grammatical inference– to measure linguistic complexity from a developmental point of view. We focus on relative complexity by considering a child learner in the process of first language acquisition. The relevance of grammatical inference models for measuring linguistic complexity from a developmental point of view is based on the fact that algorithms proposed in this area can be considered computational models for studying first language acquisition. Even though it will be possible to use different techniques from the field of machine learning as computational models for dealing with linguistic complexity -since in any model we have algorithms that can learn from data-, we claim that grammatical inference models offer some advantages over other tools.
Anthology ID:
W16-4101
Volume:
Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC)
Month:
December
Year:
2016
Address:
Osaka, Japan
Venues:
CL4LC | WS
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
1–11
Language:
URL:
https://aclanthology.org/W16-4101
DOI:
Bibkey:
Cite (ACL):
Maria Dolores Jiménez-López and Leonor Becerra-Bonache. 2016. Could Machine Learning Shed Light on Natural Language Complexity?. In Proceedings of the Workshop on Computational Linguistics for Linguistic Complexity (CL4LC), pages 1–11, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Could Machine Learning Shed Light on Natural Language Complexity? (Jiménez-López & Becerra-Bonache, 2016)
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PDF:
https://aclanthology.org/W16-4101.pdf