A multilingual evaluation dataset for monolingual word sense alignment
Date
2020-05-16Author
Ahmadi, Sina
McCrae, John P.
Nimb, Sanni
Khan, Fahad
Monachini, Monica
Pedersen, Bolette S.
Declerck, Thierry
Wissik, Tanja
Bellandi, Andrea
Pisani, Irene
Troelsgård, Thomas
Olsen, Sussi
Krek, Simon
Lipp, Veronika
Váradi, Tamás
Simon, László
Gyorffy, Andras
Tiberius, Carole
Schoonheim, Tanneke
Moshe, Yifat Ben
Rudich, Maya
Ahmad, Raya Abu
Lonke, Dorielle
Kovalenko, Kira
Langemets, Margit
Kallas, Jelena
Oksana, Dereza
Fransen, Theodorus
Cillessen, David
Lindemann, David
Alonso, Mikel
Salgado, Ana
Sancho, Jose Luis
Urena-Ruiz, Rafael-J.
Zamorano, Jordi Porta
Simov, Kiril
Osenova, Petya
Kancheva, Zara
Radev, Ivaylo
Stankovic, Ranka
Perdih, Andrej
Gabrovsek, Dejan
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Recommended Citation
Ahmadi, Sina, McCrae, John P. et al. (2020). A multilingual evaluation dataset for monolingual word sense alignment, Paper presented at the 12th International Conference on Language Resources and Evaluation (LREC), Marseille, France (11-16 May).
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Abstract
Aligning senses across resources and languages is a challenging task with beneficial applications in the field of natural language processing and electronic lexicography. In this paper, we describe our efforts in manually aligning monolingual dictionaries. The alignment is
carried out at sense-level for various resources in 15 languages. Moreover, senses are annotated with possible semantic relationships such
as broadness, narrowness, relatedness, and equivalence. In comparison to previous datasets for this task, this dataset covers a wide range
of languages and resources and focuses on the more challenging task of linking general-purpose language. We believe that our data will
pave the way for further advances in alignment and evaluation of word senses by creating new solutions, particularly those notoriously
requiring data such as neural networks. Our resources are publicly available at https://github.com/elexis-eu/MWSA.