Introducing Various Semantic Models for Amharic: Experimentation and Evaluation with Multiple Tasks and Datasets

The availability of different pre-trained semantic models has enabled the quick development of machine learning components for downstream applications. However, even if texts are abundant for low-resource languages, there are very few semantic models publicly available. Most of the publicly availabl...

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Autores principales:	Seid Muhie Yimam, Abinew Ali Ayele, Gopalakrishnan Venkatesh, Ibrahim Gashaw, Chris Biemann
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	datasets neural networks semantic models Amharic NLP low-resource language text tagging Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/544f83bbce6749a7907372d31f485747
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spelling	oai:doaj.org-article:544f83bbce6749a7907372d31f4857472021-11-25T17:39:40ZIntroducing Various Semantic Models for Amharic: Experimentation and Evaluation with Multiple Tasks and Datasets10.3390/fi131102751999-5903https://doaj.org/article/544f83bbce6749a7907372d31f4857472021-10-01T00:00:00Zhttps://www.mdpi.com/1999-5903/13/11/275https://doaj.org/toc/1999-5903The availability of different pre-trained semantic models has enabled the quick development of machine learning components for downstream applications. However, even if texts are abundant for low-resource languages, there are very few semantic models publicly available. Most of the publicly available pre-trained models are usually built as a multilingual version of semantic models that will not fit well with the need for low-resource languages. We introduce different semantic models for Amharic, a morphologically complex Ethio-Semitic language. After we investigate the publicly available pre-trained semantic models, we fine-tune two pre-trained models and train seven new different models. The models include Word2Vec embeddings, distributional thesaurus (DT), BERT-like contextual embeddings, and DT embeddings obtained via network embedding algorithms. Moreover, we employ these models for different NLP tasks and study their impact. We find that newly-trained models perform better than pre-trained multilingual models. Furthermore, models based on contextual embeddings from FLAIR and RoBERTa perform better than word2Vec models for the NER and POS tagging tasks. DT-based network embeddings are suitable for the sentiment classification task. We publicly release all the semantic models, machine learning components, and several benchmark datasets such as NER, POS tagging, sentiment classification, as well as Amharic versions of WordSim353 and SimLex999.Seid Muhie YimamAbinew Ali AyeleGopalakrishnan VenkateshIbrahim GashawChris BiemannMDPI AGarticledatasetsneural networkssemantic modelsAmharic NLPlow-resource languagetext taggingInformation technologyT58.5-58.64ENFuture Internet, Vol 13, Iss 275, p 275 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	datasets neural networks semantic models Amharic NLP low-resource language text tagging Information technology T58.5-58.64
spellingShingle	datasets neural networks semantic models Amharic NLP low-resource language text tagging Information technology T58.5-58.64 Seid Muhie Yimam Abinew Ali Ayele Gopalakrishnan Venkatesh Ibrahim Gashaw Chris Biemann Introducing Various Semantic Models for Amharic: Experimentation and Evaluation with Multiple Tasks and Datasets
description	The availability of different pre-trained semantic models has enabled the quick development of machine learning components for downstream applications. However, even if texts are abundant for low-resource languages, there are very few semantic models publicly available. Most of the publicly available pre-trained models are usually built as a multilingual version of semantic models that will not fit well with the need for low-resource languages. We introduce different semantic models for Amharic, a morphologically complex Ethio-Semitic language. After we investigate the publicly available pre-trained semantic models, we fine-tune two pre-trained models and train seven new different models. The models include Word2Vec embeddings, distributional thesaurus (DT), BERT-like contextual embeddings, and DT embeddings obtained via network embedding algorithms. Moreover, we employ these models for different NLP tasks and study their impact. We find that newly-trained models perform better than pre-trained multilingual models. Furthermore, models based on contextual embeddings from FLAIR and RoBERTa perform better than word2Vec models for the NER and POS tagging tasks. DT-based network embeddings are suitable for the sentiment classification task. We publicly release all the semantic models, machine learning components, and several benchmark datasets such as NER, POS tagging, sentiment classification, as well as Amharic versions of WordSim353 and SimLex999.
format	article
author	Seid Muhie Yimam Abinew Ali Ayele Gopalakrishnan Venkatesh Ibrahim Gashaw Chris Biemann
author_facet	Seid Muhie Yimam Abinew Ali Ayele Gopalakrishnan Venkatesh Ibrahim Gashaw Chris Biemann
author_sort	Seid Muhie Yimam
title	Introducing Various Semantic Models for Amharic: Experimentation and Evaluation with Multiple Tasks and Datasets
title_short	Introducing Various Semantic Models for Amharic: Experimentation and Evaluation with Multiple Tasks and Datasets
title_full	Introducing Various Semantic Models for Amharic: Experimentation and Evaluation with Multiple Tasks and Datasets
title_fullStr	Introducing Various Semantic Models for Amharic: Experimentation and Evaluation with Multiple Tasks and Datasets
title_full_unstemmed	Introducing Various Semantic Models for Amharic: Experimentation and Evaluation with Multiple Tasks and Datasets
title_sort	introducing various semantic models for amharic: experimentation and evaluation with multiple tasks and datasets
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/544f83bbce6749a7907372d31f485747
work_keys_str_mv	AT seidmuhieyimam introducingvarioussemanticmodelsforamharicexperimentationandevaluationwithmultipletasksanddatasets AT abinewaliayele introducingvarioussemanticmodelsforamharicexperimentationandevaluationwithmultipletasksanddatasets AT gopalakrishnanvenkatesh introducingvarioussemanticmodelsforamharicexperimentationandevaluationwithmultipletasksanddatasets AT ibrahimgashaw introducingvarioussemanticmodelsforamharicexperimentationandevaluationwithmultipletasksanddatasets AT chrisbiemann introducingvarioussemanticmodelsforamharicexperimentationandevaluationwithmultipletasksanddatasets
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Introducing Various Semantic Models for Amharic: Experimentation and Evaluation with Multiple Tasks and Datasets

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