@inproceedings{eskander-etal-2020-morphagram,
title = "{M}orph{AG}ram, Evaluation and Framework for Unsupervised Morphological Segmentation",
author = "Eskander, Ramy and
Callejas, Francesca and
Nichols, Elizabeth and
Klavans, Judith and
Muresan, Smaranda",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.879",
pages = "7112--7122",
abstract = "Computational morphological segmentation has been an active research topic for decades as it is beneficial for many natural language processing tasks. With the high cost of manually labeling data for morphology and the increasing interest in low-resource languages, unsupervised morphological segmentation has become essential for processing a typologically diverse set of languages, whether high-resource or low-resource. In this paper, we present and release MorphAGram, a publicly available framework for unsupervised morphological segmentation that uses Adaptor Grammars (AG) and is based on the work presented by Eskander et al. (2016). We conduct an extensive quantitative and qualitative evaluation of this framework on 12 languages and show that the framework achieves state-of-the-art results across languages of different typologies (from fusional to polysynthetic and from high-resource to low-resource).",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<abstract>Computational morphological segmentation has been an active research topic for decades as it is beneficial for many natural language processing tasks. With the high cost of manually labeling data for morphology and the increasing interest in low-resource languages, unsupervised morphological segmentation has become essential for processing a typologically diverse set of languages, whether high-resource or low-resource. In this paper, we present and release MorphAGram, a publicly available framework for unsupervised morphological segmentation that uses Adaptor Grammars (AG) and is based on the work presented by Eskander et al. (2016). We conduct an extensive quantitative and qualitative evaluation of this framework on 12 languages and show that the framework achieves state-of-the-art results across languages of different typologies (from fusional to polysynthetic and from high-resource to low-resource).</abstract>
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%0 Conference Proceedings
%T MorphAGram, Evaluation and Framework for Unsupervised Morphological Segmentation
%A Eskander, Ramy
%A Callejas, Francesca
%A Nichols, Elizabeth
%A Klavans, Judith
%A Muresan, Smaranda
%S Proceedings of the 12th Language Resources and Evaluation Conference
%D 2020
%8 may
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F eskander-etal-2020-morphagram
%X Computational morphological segmentation has been an active research topic for decades as it is beneficial for many natural language processing tasks. With the high cost of manually labeling data for morphology and the increasing interest in low-resource languages, unsupervised morphological segmentation has become essential for processing a typologically diverse set of languages, whether high-resource or low-resource. In this paper, we present and release MorphAGram, a publicly available framework for unsupervised morphological segmentation that uses Adaptor Grammars (AG) and is based on the work presented by Eskander et al. (2016). We conduct an extensive quantitative and qualitative evaluation of this framework on 12 languages and show that the framework achieves state-of-the-art results across languages of different typologies (from fusional to polysynthetic and from high-resource to low-resource).
%U https://aclanthology.org/2020.lrec-1.879
%P 7112-7122
Markdown (Informal)
[MorphAGram, Evaluation and Framework for Unsupervised Morphological Segmentation](https://aclanthology.org/2020.lrec-1.879) (Eskander et al., LREC 2020)
ACL