@article{moore-etal-2026-creating,
    title = "Creating a Hybrid Rule and Neural Network Based Semantic Tagger Using Silver Standard Data: The {P}y{MUSAS} Framework for Multilingual Semantic Annotation",
    author = "Moore, Andrew  and
      Rayson, Paul  and
      Archer, Dawn  and
      Czerniak, Tim  and
      Knight, Dawn  and
      Lal, Daisy Monika  and
      {\'O} Donnchadha, Gear{\'o}id  and
      {\'O} Meachair, M{\'i}che{\'a}l J.  and
      Piao, Scott  and
      U{\'i} Dhonnchadha, Elaine  and
      Vuorinen, Johanna  and
      Yabo, Yan  and
      Yang, Xiaobin",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    journal = "International Conference on Language Resources and Evaluation",
    volume = "main",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-lrec/2026.lrec-main.926/",
    pages = "11822--11833",
    abstract = "Word Sense Disambiguation (WSD) has been widely evaluated using the semantic frameworks of WordNet, BabelNet, and the Oxford Dictionary of English. However, for the UCREL Semantic Analysis System (USAS) framework, no open extensive evaluation has been performed beyond lexical coverage or single language evaluation. In this work, we perform the largest semantic tagging evaluation of the rule based system that uses the lexical resources in the USAS framework covering five different languages using four existing datasets and one novel Chinese dataset. We create a new silver labelled English dataset, to overcome the lack of manually tagged training data, that we train and evaluate various mono and multilingual neural models in both mono and cross-lingual evaluation setups with comparisons to their rule based counterparts, and show how a rule based system can be enhanced with a neural network model. The resulting neural network models, including the data they were trained on, the Chinese evaluation dataset, and all of the code will be released as open resources."
}