@inproceedings{st-arnaud-etal-2017-identifying,
title = "Identifying Cognate Sets Across Dictionaries of Related Languages",
author = "St Arnaud, Adam and
Beck, David and
Kondrak, Grzegorz",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1267",
doi = "10.18653/v1/D17-1267",
pages = "2519--2528",
abstract = "We present a system for identifying cognate sets across dictionaries of related languages. The likelihood of a cognate relationship is calculated on the basis of a rich set of features that capture both phonetic and semantic similarity, as well as the presence of regular sound correspondences. The similarity scores are used to cluster words from different languages that may originate from a common proto-word. When tested on the Algonquian language family, our system detects 63{\%} of cognate sets while maintaining cluster purity of 70{\%}.",
}
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%0 Conference Proceedings
%T Identifying Cognate Sets Across Dictionaries of Related Languages
%A St Arnaud, Adam
%A Beck, David
%A Kondrak, Grzegorz
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F st-arnaud-etal-2017-identifying
%X We present a system for identifying cognate sets across dictionaries of related languages. The likelihood of a cognate relationship is calculated on the basis of a rich set of features that capture both phonetic and semantic similarity, as well as the presence of regular sound correspondences. The similarity scores are used to cluster words from different languages that may originate from a common proto-word. When tested on the Algonquian language family, our system detects 63% of cognate sets while maintaining cluster purity of 70%.
%R 10.18653/v1/D17-1267
%U https://aclanthology.org/D17-1267
%U https://doi.org/10.18653/v1/D17-1267
%P 2519-2528
Markdown (Informal)
[Identifying Cognate Sets Across Dictionaries of Related Languages](https://aclanthology.org/D17-1267) (St Arnaud et al., EMNLP 2017)
ACL