@inproceedings{outsios-etal-2020-evaluation,
title = "Evaluation of {G}reek Word Embeddings",
author = "Outsios, Stamatis and
Karatsalos, Christos and
Skianis, Konstantinos and
Vazirgiannis, Michalis",
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-1.310",
pages = "2543--2551",
abstract = "Since word embeddings have been the most popular input for many NLP tasks, evaluating their quality is critical. Most research efforts are focusing on English word embeddings. This paper addresses the problem of training and evaluating such models for the Greek language. We present a new word analogy test set considering the original English Word2vec analogy test set and some specific linguistic aspects of the Greek language as well. Moreover, we create a Greek version of WordSim353 test collection for a basic evaluation of word similarities. Produced resources are available for download. We test seven word vector models and our evaluation shows that we are able to create meaningful representations. Last, we discover that the morphological complexity of the Greek language and polysemy can influence the quality of the resulting word embeddings.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<abstract>Since word embeddings have been the most popular input for many NLP tasks, evaluating their quality is critical. Most research efforts are focusing on English word embeddings. This paper addresses the problem of training and evaluating such models for the Greek language. We present a new word analogy test set considering the original English Word2vec analogy test set and some specific linguistic aspects of the Greek language as well. Moreover, we create a Greek version of WordSim353 test collection for a basic evaluation of word similarities. Produced resources are available for download. We test seven word vector models and our evaluation shows that we are able to create meaningful representations. Last, we discover that the morphological complexity of the Greek language and polysemy can influence the quality of the resulting word embeddings.</abstract>
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%0 Conference Proceedings
%T Evaluation of Greek Word Embeddings
%A Outsios, Stamatis
%A Karatsalos, Christos
%A Skianis, Konstantinos
%A Vazirgiannis, Michalis
%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 outsios-etal-2020-evaluation
%X Since word embeddings have been the most popular input for many NLP tasks, evaluating their quality is critical. Most research efforts are focusing on English word embeddings. This paper addresses the problem of training and evaluating such models for the Greek language. We present a new word analogy test set considering the original English Word2vec analogy test set and some specific linguistic aspects of the Greek language as well. Moreover, we create a Greek version of WordSim353 test collection for a basic evaluation of word similarities. Produced resources are available for download. We test seven word vector models and our evaluation shows that we are able to create meaningful representations. Last, we discover that the morphological complexity of the Greek language and polysemy can influence the quality of the resulting word embeddings.
%U https://aclanthology.org/2020.lrec-1.310
%P 2543-2551
Markdown (Informal)
[Evaluation of Greek Word Embeddings](https://aclanthology.org/2020.lrec-1.310) (Outsios et al., LREC 2020)
ACL
- Stamatis Outsios, Christos Karatsalos, Konstantinos Skianis, and Michalis Vazirgiannis. 2020. Evaluation of Greek Word Embeddings. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 2543–2551, Marseille, France. European Language Resources Association.