Hybrid lemmatization in HuSpaCy
Péter Berkecz, Orosz, György, Zsolt Szántó, Szabó, Gergő, Richárd Farkas · arXiv (Cornell University) · 2023
Lemmatization is still not a trivial task for morphologically rich languages. Previous studies showed that hybrid architectures usually work better for these languages and can yield great results. This paper presents a hybrid lemmatizer utilizing both a neural model, dictionaries and hand-crafted rules. We introduce a hybrid architecture along with empirical results on a widely used Hungarian dataset. The presented methods are published as three HuSpaCy models.