1. A Multilingual Evaluation Dataset for Monolingual Word Sense Alignment
- Author
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Ahmadi, S., Mccrae, J. P., Nimb, S., Khan, F., Monachini, M., Pedersen, B. S., Declerck, T., Wissik, T., Bellandi, A., Pisani, I., Troelsgård, T., Olsen, S., Krek, S., Lipp, V., Váradi, T., Simon, L., Gyorffy, A., Tiberius, C., Schoonheim, T., Moshe, Y. B., Rudich, M., Ahmad, R. A., Lonke, D., Kovalenko, K., Langemets, M., Kallas, J., Dereza, O., Fransen, T., Cillessen, D., Lindemann, D., Alonso, M., Ana Salgado, Sancho, J. L., Ureña-Ruiz, R. J., Zamorano, J. P., Simov, K., Osenova, P., Kancheva, Z., Radev, I., Stankovi, R., Perdih, A., Gabrovšek, D., Horizon 2020, Bulgarian National Interdisciplinary Research e-Infrastructure for Resources and Technologies, Science Foundation Ireland, Irish Research Council, Centro de Linguística da UNL (CLUNL), and Calzolari, Nicoletta
- Subjects
050101 languages & linguistics ,Lexicography ,lexical semantic resources ,Language resource ,05 social sciences ,language resource ,02 engineering and technology ,sense alignment ,Lexical semantics resoruces ,lexicography ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,0501 psychology and cognitive sciences ,Sense alignment - 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 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.