Building MUSCLE, a Dataset for MUltilingual Semantic Classification of Links between Entities

Lucia Pitarch, Carlos Bobed Lisbona, David Abián, Jorge Gracia, Jordi Bernad

Abstract
In this paper we introduce MUSCLE, a dataset for MUltilingual lexico-Semantic Classification of Links between Entities. The MUSCLE dataset was designed to train and evaluate Lexical Relation Classification (LRC) systems with 27K pairs of universal concepts selected from Wikidata, a large and highly multilingual factual Knowledge Graph (KG). Each pair of concepts includes its lexical forms in 25 languages and is labeled with up to five possible lexico-semantic relations between the concepts: hypernymy, hyponymy, meronymy, holonymy, and antonymy. Inspired by Semantic Map theory, the dataset bridges lexical and conceptual semantics, is more challenging and robust than previous datasets for LRC, avoids lexical memorization, is domain-balanced across entities, and enables enrichment and hierarchical information retrieval.
Anthology ID:
2024.lrec-main.233
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
2580–2594
Language:
URL:
https://aclanthology.org/2024.lrec-main.233
DOI:
Bibkey:
Cite (ACL):
Lucia Pitarch, Carlos Bobed Lisbona, David Abián, Jorge Gracia, and Jordi Bernad. 2024. Building MUSCLE, a Dataset for MUltilingual Semantic Classification of Links between Entities. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 2580–2594, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Building MUSCLE, a Dataset for MUltilingual Semantic Classification of Links between Entities (Pitarch et al., LREC-COLING 2024)
Copy Citation:
PDF:
https://preview.aclanthology.org/dois-2013-emnlp/2024.lrec-main.233.pdf
PDF Search