A Multilingual Evaluation Dataset for Monolingual Word Sense Alignment
Sina Ahmadi, John Philip McCrae, Sanni Nimb, Fahad Khan, Monica Monachini, Bolette Pedersen, Thierry Declerck, Tanja Wissik, Bell, Andrea i, Irene Pisani, Thomas Troelsg{\aa}rd, Sussi Olsen, Simon Krek, Veronika Lipp, Tam{\'a}s V{\'a}radi, L{\'a}szl{\'o} Simon, Andr{\'a}s Gyorffy, Carole Tiberius, Tanneke Schoonheim, Yifat Ben Moshe, Maya Rudich, Raya Abu Ahmad, Dorielle Lonke, Kira Kovalenko, Margit Langemets, Jelena Kallas, Oksana Dereza, Theodorus Fransen, David Cillessen, David Lindemann, Mikel Alonso, Ana Salgado, Jos{\'e} Luis Sancho, Rafael-J. Ure{\~n}a-Ruiz, Jordi Porta Zamorano, Kiril Simov, Petya Osenova, Zara Kancheva, Ivaylo Radev, Ranka Stankovi{\'c}, Andrej Perdih, Dejan Gabrovsek
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/elexis-eu/MWSAOfficialIn papernone★ 12
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.