Abstract
Antonymy-Synonymy Discrimination (ASD) is a challenging NLP task that has been tackled mainly for English. We present a dataset for ASD in Spanish, built using online dictionaries and Wordnet in Spanish. To evaluate the quality of the dataset, we performed two manual annotations on a random sample of the dataset, obtaining 0.89 of kappa score between them. Additionally, each annotator obtained 0.9 of agreement according to the dataset. The dataset is split into train, val and test using three algorithms: (1) randomly, (2) Shwartz’s split [20], and (3) graph split. The last two are without lexical intersection between its parts, and (3) is based on the topology of the antonymy relation depicted from data. Finally, we report results using a parasiamese neural network [6] in our dataset, one of the best performing approaches in the literature.
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Notes
- 1.
[8] addressed lexical relations detection and performed a manually curated automatic translation of the dataset to spanish and german and only consists of 1850 pairs of antonyms.
- 2.
The meanings were obtained through the Real Academia Española dictionary (https://dle.rae.es/).
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Camacho, J., Cámera, J., Etcheverry, M. (2022). Antonymy-Synonymy Discrimination in Spanish with a Parasiamese Network. In: Bicharra Garcia, A.C., Ferro, M., Rodríguez Ribón, J.C. (eds) Advances in Artificial Intelligence – IBERAMIA 2022. IBERAMIA 2022. Lecture Notes in Computer Science(), vol 13788. Springer, Cham. https://doi.org/10.1007/978-3-031-22419-5_24
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