B-Morpher: Automated Learning of Morphological Language Characteristics for Inflection and Morphological Analysis

The automated induction of inflection rules is an important research area for computational linguistics. In this paper, we present a novel morphological rule induction model called B-Morpher that can be used for both inflection analysis and morphological analysis. The core element of the engine is a modified Bayes classifier in which class categories correspond to general string transformation rules. Beside the core classification module, the engine contains a neural network module and verification unit to improve classification accuracy. For the evaluation, beside the large Hungarian dataset the tests include smaller non-Hungarian datasets from the SIGMORPHON shared task pools. Our evaluation shows that the efficiency of B-Morpher is comparable with the best results, and it outperforms the state-of-the-art base models for some languages. The proposed system can be characterized by not only high accuracy, but also short training time and small knowledge base size.

@article{kovacs2022b,
  title={B-Morpher: Automated Learning of Morphological Language Characteristics for Inflection and Morphological Analysis},
  author={Kov{'a}cs, L{'a}szl{'o} and Szab{'o}, G{'a}bor},
  journal={Cybernetics and Information Technologies},
  volume={22},
  number={4},
  pages={111--128},
  year={2022}
}