A Multilingual Evaluation Dataset for Monolingual Word Sense Alignment
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Documents
- 2020.lrec-1.395
Final published version, 685 KB, PDF document
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.
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.
Original language | English |
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Title of host publication | Proceedings of the 12th Language Resources and Evaluation Conference |
Editors | Nicoletta Calzolari |
Number of pages | 10 |
Place of Publication | Marseille, France |
Publisher | European Language Resources Association |
Publication date | 2020 |
Pages | 3232-3242 |
ISBN (Electronic) | 979-10-95546-34-4 |
Publication status | Published - 2020 |
Links
- http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.395.pdf
Final published version
ID: 241583424