Automatic Animacy Classification for Latvian Nouns

We introduce the first automatic animacy classifier for the Latvian language. Animacy, a linguistic feature indicating whether a noun refers to a living entity, plays an important role in Latvian grammatical structures and syntactic agreement, but remains unexplored in Latvian NLP. We adapt and extend existing methods to develop type-based animacy classifiers that distinguish between human and non-human nouns. Due to the limited utility of Latvian WordNet, the classifier’s training data was derived from the WordNets of Lithuanian, English, and Japanese. These lists were intersected and mapped to Latvian nouns from the Tēzaurs dictionary through automatic translation. The resulting dataset was used to train classifiers with fastText and LVBERT embeddings. Results show good performance from a MLP classifier using the last four layers of LVBERT, with Lithuanian data contributing more than English. This demonstrates a viable method for animacy classification in languages lacking robust lexical resources and shows potential for broader application in morphologically rich, under-resourced languages.