@inproceedings{loukatou-etal-2019-word,
title = "Is Word Segmentation Child{'}s Play in All Languages?",
author = "Loukatou, Georgia R. and
Moran, Steven and
Blasi, Damian and
Stoll, Sabine and
Cristia, Alejandrina",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1383",
doi = "10.18653/v1/P19-1383",
pages = "3931--3937",
abstract = "When learning language, infants need to break down the flow of input speech into minimal word-like units, a process best described as unsupervised bottom-up segmentation. Proposed strategies include several segmentation algorithms, but only cross-linguistically robust algorithms could be plausible candidates for human word learning, since infants have no initial knowledge of the ambient language. We report on the stability in performance of 11 conceptually diverse algorithms on a selection of 8 typologically distinct languages. The results consist evidence that some segmentation algorithms are cross-linguistically valid, thus could be considered as potential strategies employed by all infants.",
}
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<abstract>When learning language, infants need to break down the flow of input speech into minimal word-like units, a process best described as unsupervised bottom-up segmentation. Proposed strategies include several segmentation algorithms, but only cross-linguistically robust algorithms could be plausible candidates for human word learning, since infants have no initial knowledge of the ambient language. We report on the stability in performance of 11 conceptually diverse algorithms on a selection of 8 typologically distinct languages. The results consist evidence that some segmentation algorithms are cross-linguistically valid, thus could be considered as potential strategies employed by all infants.</abstract>
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%0 Conference Proceedings
%T Is Word Segmentation Child’s Play in All Languages?
%A Loukatou, Georgia R.
%A Moran, Steven
%A Blasi, Damian
%A Stoll, Sabine
%A Cristia, Alejandrina
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F loukatou-etal-2019-word
%X When learning language, infants need to break down the flow of input speech into minimal word-like units, a process best described as unsupervised bottom-up segmentation. Proposed strategies include several segmentation algorithms, but only cross-linguistically robust algorithms could be plausible candidates for human word learning, since infants have no initial knowledge of the ambient language. We report on the stability in performance of 11 conceptually diverse algorithms on a selection of 8 typologically distinct languages. The results consist evidence that some segmentation algorithms are cross-linguistically valid, thus could be considered as potential strategies employed by all infants.
%R 10.18653/v1/P19-1383
%U https://aclanthology.org/P19-1383
%U https://doi.org/10.18653/v1/P19-1383
%P 3931-3937
Markdown (Informal)
[Is Word Segmentation Child’s Play in All Languages?](https://aclanthology.org/P19-1383) (Loukatou et al., ACL 2019)
ACL
- Georgia R. Loukatou, Steven Moran, Damian Blasi, Sabine Stoll, and Alejandrina Cristia. 2019. Is Word Segmentation Child’s Play in All Languages?. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3931–3937, Florence, Italy. Association for Computational Linguistics.