Hybrid lemmatization in HuSpaCy
Péter Berkecz György Orosz Zsolt Szántó Gergő Szabó Richárd Farkas
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.