A Multilingual Evaluation Dataset for Monolingual Word Sense Alignment

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 iscarried out at sense-level for various resources in 15 languages. Moreover, senses are annotated with possible semantic relationships suchas broadness, narrowness, relatedness, and equivalence. In comparison to previous datasets for this task, this dataset covers a wide rangeof languages and resources and focuses on the more challenging task of linking general-purpose language. We believe that our data willpave the way for further advances in alignment and evaluation of word senses by creating new solutions, particularly those notoriouslyrequiring data such as neural networks. Our resources are publicly available at https://github.com/elexis-eu/MWSA. 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 iscarried out at sense-level for various resources in 15 languages. Moreover, senses are annotated with possible semantic relationships suchas broadness, narrowness, relatedness, and equivalence. In comparison to previous datasets for this task, this dataset covers a wide rangeof languages and resources and focuses on the more challenging task of linking general-purpose language. We believe that our data willpave the way for further advances in alignment and evaluation of word senses by creating new solutions, particularly those notoriouslyrequiring data such as neural networks. Our resources are publicly available at https://github.com/elexis-eu/MWSA.