VoxEL: A benchmark dataset for multilingual entity linking
Author
dc.contributor.author
Rosales-Méndez, Henry
Author
dc.contributor.author
Hogan, Aidan
Author
dc.contributor.author
Poblete Labra, Bárbara
Admission date
dc.date.accessioned
2019-05-31T15:21:01Z
Available date
dc.date.available
2019-05-31T15:21:01Z
Publication date
dc.date.issued
2018
Cita de ítem
dc.identifier.citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Volumen 11137 LNCS, 2018, Pages 170-186.
Identifier
dc.identifier.issn
16113349
Identifier
dc.identifier.issn
03029743
Identifier
dc.identifier.other
10.1007/978-3-030-00668-6_11
Identifier
dc.identifier.uri
https://repositorio.uchile.cl/handle/2250/169479
Abstract
dc.description.abstract
The Entity Linking (EL) task identifies entity mentions in a
text corpus and associates them with corresponding entities in a given
knowledge base. While traditional EL approaches have largely focused
on English texts, current trends are towards language-agnostic or otherwise multilingual approaches that can perform EL over texts in many
languages. One of the obstacles to ongoing research on multilingual EL
is a scarcity of annotated datasets with the same text in different languages. In this work we thus propose VoxEL: a manually-annotated gold
standard for multilingual EL featuring the same text expressed in five
European languages. We first motivate and describe the VoxEL dataset,
using it to compare the behaviour of state of the art EL (multilingual)
systems for five different languages, contrasting these results with those
obtained using machine translation to English. Overall, our results identify how five state-of-the-art multilingual EL systems compare for various
languages, how the results of different languages compare, and further
suggest that machine translation of input text to English is now a competitive alternative to dedicated multilingual EL configurations.