Abstract
Aligning lexical resources that associate words with concepts in multiple languages increases the total amount of semantic information that can be leveraged for various NLP tasks. We present a translation-based approach to mapping concepts across diverse resources. Our methods depend only on multilingual lexicalization information. When applied to align WordNet/BabelNet to CLICS and OmegaWiki, our methods achieve state-of-the-art accuracy, without any dependence on other sources of semantic knowledge. Since each word-concept pair corresponds to a unique sense of the word, we also demonstrate that the mapping task can be framed as word sense disambiguation. To facilitate future work, we release a set of high-precision WordNet-CLICS alignments, produced by combining three different mapping methods.- Anthology ID:
- 2022.lrec-1.774
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 7147–7154
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.774
- DOI:
- Cite (ACL):
- Hongchang Bao, Bradley Hauer, and Grzegorz Kondrak. 2022. Lexical Resource Mapping via Translations. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 7147–7154, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Lexical Resource Mapping via Translations (Bao et al., LREC 2022)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/2022.lrec-1.774.pdf
- Data
- Concepticon