@inproceedings{snajder-2014-derivbase,
    title = "{D}eriv{B}ase.hr: A High-Coverage Derivational Morphology Resource for {C}roatian",
    author = "{\v{S}}najder, Jan",
    editor = "Calzolari, Nicoletta  and
      Choukri, Khalid  and
      Declerck, Thierry  and
      Loftsson, Hrafn  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}`14)",
    month = may,
    year = "2014",
    address = "Reykjavik, Iceland",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/L14-1068/",
    pages = "3371--3377",
    abstract = "Knowledge about derivational morphology has been proven useful for a number of natural language processing (NLP) tasks. We describe the construction and evaluation of DerivBase.hr, a large-coverage morphological resource for Croatian. DerivBase.hr groups 100k lemmas from web corpus hrWaC into 56k clusters of derivationally related lemmas, so-called derivational families. We focus on suffixal derivation between and within nouns, verbs, and adjectives. We propose two approaches: an unsupervised approach and a knowledge-based approach based on a hand-crafted morphology model but without using any additional lexico-semantic resources The resource acquisition procedure consists of three steps: corpus preprocessing, acquisition of an inflectional lexicon, and the induction of derivational families. We describe an evaluation methodology based on manually constructed derivational families from which we sample and annotate pairs of lemmas. We evaluate DerivBase.hr on the so-obtained sample, and show that the knowledge-based version attains good clustering quality of 81.2{\%} precision, 76.5{\%} recall, and 78.8{\%} F1 -score. As with similar resources for other languages, we expect DerivBase.hr to be useful for a number of NLP tasks."
}