Work

On the Correlation of Word Embedding Evaluation Metrics

Year: 2020

Type: conference-paper

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.

Source: HAL

Authors: Torregrossa, François, Claveau, Vincent, Kooli, Nihel, Gravier, Guillaume, Allesiardo, Robin

Institution: LINKMEDIA: Creating and exploiting explicit links between multimedia fragments

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W3028770966

FWCI: 0.418

Cites: 38

Cited by: 5

Related to: 10