@inproceedings{torregrossa-etal-2020-correlation,
title = "On the Correlation of Word Embedding Evaluation Metrics",
author = "Torregrossa, Fran{\c{c}}ois and
Claveau, Vincent and
Kooli, Nihel and
Gravier, Guillaume and
Allesiardo, Robin",
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-1.589",
pages = "4789--4797",
abstract = "Word embeddings intervene in a wide range of natural language processing tasks. These geometrical representations are easy to manipulate for automatic systems. Therefore, they quickly invaded all areas of language processing. While they surpass all predecessors, it is still not straightforward why and how they do so. In this article, we propose to investigate all kind of evaluation metrics on various datasets in order to discover how they correlate with each other. Those correlations lead to 1) a fast solution to select the best word embeddings among many others, 2) a new criterion that may improve the current state of static Euclidean word embeddings, and 3) a way to create a set of complementary datasets, i.e. each dataset quantifies a different aspect of word embeddings.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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%0 Conference Proceedings
%T On the Correlation of Word Embedding Evaluation Metrics
%A Torregrossa, François
%A Claveau, Vincent
%A Kooli, Nihel
%A Gravier, Guillaume
%A Allesiardo, Robin
%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 torregrossa-etal-2020-correlation
%X Word embeddings intervene in a wide range of natural language processing tasks. These geometrical representations are easy to manipulate for automatic systems. Therefore, they quickly invaded all areas of language processing. While they surpass all predecessors, it is still not straightforward why and how they do so. In this article, we propose to investigate all kind of evaluation metrics on various datasets in order to discover how they correlate with each other. Those correlations lead to 1) a fast solution to select the best word embeddings among many others, 2) a new criterion that may improve the current state of static Euclidean word embeddings, and 3) a way to create a set of complementary datasets, i.e. each dataset quantifies a different aspect of word embeddings.
%U https://aclanthology.org/2020.lrec-1.589
%P 4789-4797
Markdown (Informal)
[On the Correlation of Word Embedding Evaluation Metrics](https://aclanthology.org/2020.lrec-1.589) (Torregrossa et al., LREC 2020)
ACL
- François Torregrossa, Vincent Claveau, Nihel Kooli, Guillaume Gravier, and Robin Allesiardo. 2020. On the Correlation of Word Embedding Evaluation Metrics. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 4789–4797, Marseille, France. European Language Resources Association.