@inproceedings{rama-etal-2018-automatic,
title = "Are Automatic Methods for Cognate Detection Good Enough for Phylogenetic Reconstruction in Historical Linguistics?",
author = {Rama, Taraka and
List, Johann-Mattis and
Wahle, Johannes and
J{\"a}ger, Gerhard},
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2063",
doi = "10.18653/v1/N18-2063",
pages = "393--400",
abstract = "We evaluate the performance of state-of-the-art algorithms for automatic cognate detection by comparing how useful automatically inferred cognates are for the task of phylogenetic inference compared to classical manually annotated cognate sets. Our findings suggest that phylogenies inferred from automated cognate sets come close to phylogenies inferred from expert-annotated ones, although on average, the latter are still superior. We conclude that future work on phylogenetic reconstruction can profit much from automatic cognate detection. Especially where scholars are merely interested in exploring the bigger picture of a language family{'}s phylogeny, algorithms for automatic cognate detection are a useful complement for current research on language phylogenies.",
}
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<abstract>We evaluate the performance of state-of-the-art algorithms for automatic cognate detection by comparing how useful automatically inferred cognates are for the task of phylogenetic inference compared to classical manually annotated cognate sets. Our findings suggest that phylogenies inferred from automated cognate sets come close to phylogenies inferred from expert-annotated ones, although on average, the latter are still superior. We conclude that future work on phylogenetic reconstruction can profit much from automatic cognate detection. Especially where scholars are merely interested in exploring the bigger picture of a language family’s phylogeny, algorithms for automatic cognate detection are a useful complement for current research on language phylogenies.</abstract>
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%0 Conference Proceedings
%T Are Automatic Methods for Cognate Detection Good Enough for Phylogenetic Reconstruction in Historical Linguistics?
%A Rama, Taraka
%A List, Johann-Mattis
%A Wahle, Johannes
%A Jäger, Gerhard
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F rama-etal-2018-automatic
%X We evaluate the performance of state-of-the-art algorithms for automatic cognate detection by comparing how useful automatically inferred cognates are for the task of phylogenetic inference compared to classical manually annotated cognate sets. Our findings suggest that phylogenies inferred from automated cognate sets come close to phylogenies inferred from expert-annotated ones, although on average, the latter are still superior. We conclude that future work on phylogenetic reconstruction can profit much from automatic cognate detection. Especially where scholars are merely interested in exploring the bigger picture of a language family’s phylogeny, algorithms for automatic cognate detection are a useful complement for current research on language phylogenies.
%R 10.18653/v1/N18-2063
%U https://aclanthology.org/N18-2063
%U https://doi.org/10.18653/v1/N18-2063
%P 393-400
Markdown (Informal)
[Are Automatic Methods for Cognate Detection Good Enough for Phylogenetic Reconstruction in Historical Linguistics?](https://aclanthology.org/N18-2063) (Rama et al., NAACL 2018)
ACL