article · 2018
Lexical Perspective on Wordnet to Wordnet Mapping
Ewa Rudnicka, Francis Bond, Łukasz Grabowski, Maciej Piasecki, Tadeusz Piotrowski
Cited by 3Open access
Abstract
The paper presents a feature-based model of equivalence targeted at (manual) sense linking between Princeton WordNet and plWordNet.The model incorporates insights from lexicographic and translation theories on bilingual equivalence and draws on the results of earlier synsetlevel mapping of nouns between Princeton WordNet and plWordNet.It takes into account all basic aspects of language such as form, meaning and function and supplements them with (parallel) corpus frequency and translatability.Three types of equivalence are distinguished, namely strong, regular and weak depending on the conformity with the proposed features.The presented solutions are languageneutral and they can be easily applied to language pairs other than Polish and English.Sense-level mapping is a more finegrained mapping than the existing synset mappings and is thus of great potential to human and machine translation.
Cite this paper
Rudnicka, E., Bond, F., Grabowski, Ł., Piasecki, M., & Piotrowski, T. (2018). Lexical Perspective on Wordnet to Wordnet Mapping. https://doi.org/10.18653/v1/2018.gwc-1.24
Rudnicka, Ewa, et al. “Lexical Perspective on Wordnet to Wordnet Mapping.” 2018, pp. 209–18, https://doi.org/10.18653/v1/2018.gwc-1.24.
Rudnicka, Ewa, Francis Bond, Łukasz Grabowski, Maciej Piasecki, and Tadeusz Piotrowski. 2018. “Lexical Perspective on Wordnet to Wordnet Mapping.” https://doi.org/10.18653/v1/2018.gwc-1.24.
Rudnicka, E. et al. (2018) ‘Lexical Perspective on Wordnet to Wordnet Mapping’. Available at: https://doi.org/10.18653/v1/2018.gwc-1.24.
@article{rudnicka2018lexical,
title = {{Lexical Perspective on Wordnet to Wordnet Mapping}},
author = {Rudnicka, Ewa and Bond, Francis and Grabowski, Łukasz and Piasecki, Maciej and Piotrowski, Tadeusz},
pages = {209--218},
year = {2018},
doi = {10.18653/v1/2018.gwc-1.24}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.
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