Less is Better: A cognitively inspired unsupervised model for language segmentation

Jinbiao Yang, Stefan L. Frank, Antal van den Bosch


Abstract
Language users process utterances by segmenting them into many cognitive units, which vary in their sizes and linguistic levels. Although we can do such unitization/segmentation easily, its cognitive mechanism is still not clear. This paper proposes an unsupervised model, Less-is-Better (LiB), to simulate the human cognitive process with respect to language unitization/segmentation. LiB follows the principle of least effort and aims to build a lexicon which minimizes the number of unit tokens (alleviating the effort of analysis) and number of unit types (alleviating the effort of storage) at the same time on any given corpus. LiB’s workflow is inspired by empirical cognitive phenomena. The design makes the mechanism of LiB cognitively plausible and the computational requirement light-weight. The lexicon generated by LiB performs the best among different types of lexicons (e.g. ground-truth words) both from an information-theoretical view and a cognitive view, which suggests that the LiB lexicon may be a plausible proxy of the mental lexicon.
Anthology ID:
2020.cogalex-1.4
Volume:
Proceedings of the Workshop on the Cognitive Aspects of the Lexicon
Month:
December
Year:
2020
Address:
Online
Editors:
Michael Zock, Emmanuele Chersoni, Alessandro Lenci, Enrico Santus
Venue:
CogALex
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
33–45
Language:
URL:
https://aclanthology.org/2020.cogalex-1.4
DOI:
Bibkey:
Cite (ACL):
Jinbiao Yang, Stefan L. Frank, and Antal van den Bosch. 2020. Less is Better: A cognitively inspired unsupervised model for language segmentation. In Proceedings of the Workshop on the Cognitive Aspects of the Lexicon, pages 33–45, Online. Association for Computational Linguistics.
Cite (Informal):
Less is Better: A cognitively inspired unsupervised model for language segmentation (Yang et al., CogALex 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.cogalex-1.4.pdf
Code
 ray306/lib