@inproceedings{brutans-bloem-2025-automatic,
    title = "Automatic {A}nimacy Classification for {L}atvian Nouns",
    author = "Brut{\={a}}ns, Ralfs  and
      Bloem, Jelke",
    editor = "Das, Sudhansu Bala  and
      Mishra, Pruthwik  and
      Singh, Alok  and
      Muhammad, Shamsuddeen Hassan  and
      Ekbal, Asif  and
      Das, Uday Kumar",
    booktitle = "Proceedings of the Workshop on Beyond English: Natural Language Processing for all Languages in an Era of Large Language Models",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, BULGARIA",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.globalnlp-1.11/",
    pages = "90--97",
    abstract = "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 T{\={e}}zaurs 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."
}