Summary of the paper

Title A High-Coverage Derivational Morphology Resource for Croatian
Authors Jan Šnajder
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, a large-coverage morphological resource for Croatian. 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 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 to be useful for a number of NLP tasks.
Topics Lexicon, Lexical Database
Full paper A High-Coverage Derivational Morphology Resource for Croatian
Bibtex @InProceedings{NAJDER14.1090,
  author = {Jan Šnajder},
  title = { A High-Coverage Derivational Morphology Resource for Croatian},
  booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)},
  year = {2014},
  month = {may},
  date = {26-31},
  address = {Reykjavik, Iceland},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {978-2-9517408-8-4},
  language = {english}
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