MMG at SemEval-2022 Task 1: A Reverse Dictionary approach based on a review of the dataset from a lexicographic perspective

Alfonso Ardoiz, Miguel Ortega-Martín, Óscar García-Sierra, Jorge Álvarez, Ignacio Arranz, Adrián Alonso


Abstract
This paper presents a novel and linguistic-driven system for the Spanish Reverse Dictionary task of SemEval-2022 Task 1. The aim of this task is the automatic generation of a word using its gloss. The conclusion is that this task results could improve if the quality of the dataset did as well by incorporating high-quality lexicographic data. Therefore, in this paper we analyze the main gaps in the proposed dataset and describe how these limitations could be tackled.
Anthology ID:
2022.semeval-1.7
Volume:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Month:
July
Year:
2022
Address:
Seattle, United States
Editors:
Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
68–74
Language:
URL:
https://aclanthology.org/2022.semeval-1.7
DOI:
10.18653/v1/2022.semeval-1.7
Bibkey:
Cite (ACL):
Alfonso Ardoiz, Miguel Ortega-Martín, Óscar García-Sierra, Jorge Álvarez, Ignacio Arranz, and Adrián Alonso. 2022. MMG at SemEval-2022 Task 1: A Reverse Dictionary approach based on a review of the dataset from a lexicographic perspective. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 68–74, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
MMG at SemEval-2022 Task 1: A Reverse Dictionary approach based on a review of the dataset from a lexicographic perspective (Ardoiz et al., SemEval 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.semeval-1.7.pdf