Skip to main content Skip to main navigation

Publikation

A Multilingual Evaluation Dataset for Monolingual Word Sense Alignment

Sina Ahmadi; John P. McCrae; Sanni Nimb; Fahad Khan; Monica Monachini; Bolette S. Pedersen; Thierry Declerck; Tanja Wissik; Andrea Bellandi; Irene Pisani; Thomas Troelsgård; Sussi Olsen; Simon Krek; Veronika Lipp; Tamás Váradi; László Simon; András Győrffy; 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; José Luis Sancho; Rafael-J. Ureña-Ruiz; Jordi Porta Zamorano; Kiril Simov; Petya Osenova; Zara Kancheva; Ivaylo Radev; Ranka Stanković; Andrej Perdih; Dejan Gabrovšek
In: Nicoletta Calzolari; Frédéric Béchet; Philippe Blache; Khalid Choukri; Christopher Cieri; Thierry Declerck; Sara Goggi; Hitoshi Isahara; Bente Maegaard; Joseph Mariani; Hélène Mazo; Asuncion Moreno; Jan Odijk; Stelios Piperidis (Hrsg.). Proceedings of the Twelfth International Conference on Language Resources and Evaluation (LREC 2020). International Conference on Language Resources and Evaluation (LREC-2020), May 11-16, Marseile, France, Pages 3232-3242, ISBN 979-10-95546-34-4, ELRA, Paris, 5/2020.

Zusammenfassung

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.

Projekte