@inproceedings{yildirim-etal-2026-automatic,
    title = "Automatic Lemmatisation for {N}orwegian",
    author = "Yildirim, Ahmet  and
      Hagen, Kristin  and
      Haug, Dag",
    editor = "Hinrichs, Erhard  and
      Nivre, Joakim  and
      Osenova, Petya  and
      Pustejovsky, James  and
      Zinn, Claus",
    booktitle = "Proceedings of the Workshop on Structured Linguistic Data and Evaluation ({SL}i{DE})",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/revision-previews/2026.slide-1.8/",
    doi = "10.63317/2cpfp5inka2c",
    pages = "93--103",
    abstract = "We report on a new lemmatisation system for Norwegian, which is a particularly challenging language with two written standards, Bokm{\r{a}}l and Nynorsk, that both have a lot of optionality. Our system covers both varieties and consists of a neural model that classifies words into rewrite rule classes that produce their lemma, as well as a large-scale computational lexicon of Norwegian that gives all possible inflections of a large part of the Norwegian vocabulary. We test different ways of combining these components. When evaluated with pure string-matching against the lemmas in the gold data, all systems perform approximately at the same level (99.1-99.2{\%} on Bokm{\r{a}}l and 98.5-98.6{\%} on Nynorsk), but detailed error analysis shows that the computational lexicon reduces the number of true errors by more than half (reaching 99.6{\%} accuracy on Bokm{\r{a}}l and 99.3{\%} on Nynorsk), as opposed to ``surface errors'' like using a different, but equally acceptable spelling variant of the correct lemma."
}