@inproceedings{mersha-wu-2020-morphology,
title = "Morphology-rich Alphasyllabary Embeddings",
author = "Mersha, Amanuel and
Wu, Stephen",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.315",
pages = "2590--2595",
abstract = "Word embeddings have been successfully trained in many languages. However, both intrinsic and extrinsic metrics are variable across languages, especially for languages that depart significantly from English in morphology and orthography. This study focuses on building a word embedding model suitable for the Semitic language of Amharic (Ethiopia), which is both morphologically rich and written as an alphasyllabary (abugida) rather than an alphabet. We compare embeddings from tailored neural models, simple pre-processing steps, off-the-shelf baselines, and parallel tasks on a better-resourced Semitic language {--} Arabic. Experiments show our model{'}s performance on word analogy tasks, illustrating the divergent objectives of morphological vs. semantic analogies.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<abstract>Word embeddings have been successfully trained in many languages. However, both intrinsic and extrinsic metrics are variable across languages, especially for languages that depart significantly from English in morphology and orthography. This study focuses on building a word embedding model suitable for the Semitic language of Amharic (Ethiopia), which is both morphologically rich and written as an alphasyllabary (abugida) rather than an alphabet. We compare embeddings from tailored neural models, simple pre-processing steps, off-the-shelf baselines, and parallel tasks on a better-resourced Semitic language – Arabic. Experiments show our model’s performance on word analogy tasks, illustrating the divergent objectives of morphological vs. semantic analogies.</abstract>
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%0 Conference Proceedings
%T Morphology-rich Alphasyllabary Embeddings
%A Mersha, Amanuel
%A Wu, Stephen
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Twelfth 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 mersha-wu-2020-morphology
%X Word embeddings have been successfully trained in many languages. However, both intrinsic and extrinsic metrics are variable across languages, especially for languages that depart significantly from English in morphology and orthography. This study focuses on building a word embedding model suitable for the Semitic language of Amharic (Ethiopia), which is both morphologically rich and written as an alphasyllabary (abugida) rather than an alphabet. We compare embeddings from tailored neural models, simple pre-processing steps, off-the-shelf baselines, and parallel tasks on a better-resourced Semitic language – Arabic. Experiments show our model’s performance on word analogy tasks, illustrating the divergent objectives of morphological vs. semantic analogies.
%U https://aclanthology.org/2020.lrec-1.315
%P 2590-2595
Markdown (Informal)
[Morphology-rich Alphasyllabary Embeddings](https://aclanthology.org/2020.lrec-1.315) (Mersha & Wu, LREC 2020)
ACL
- Amanuel Mersha and Stephen Wu. 2020. Morphology-rich Alphasyllabary Embeddings. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 2590–2595, Marseille, France. European Language Resources Association.