A multilingual evaluation dataset for monolingual word sense alignment

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).

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.

Funder

Horizon 2020
Bulgarian National Interdisciplinary Research e-Infrastructure for Resources and Technologies
Science Foundation Ireland
Irish Research Council

Publisher

National University of Ireland Galway

Rights

Attribution-NonCommercial-NoDerivs 3.0 Ireland

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Collections

Data Science Institute (Conference Papers)