Abstract

Morpher is a novel morphological rule induction engine designed and developed for agglutinative languages. The Morpher engine models inflection using general string-based transformation rules and it can learn multiple arbitrary affix types, too. In order to scale the engine to training sets containing millions of examples, we need an efficient management of the generated rule base. In this paper we investigate and present several optimization techniques using rule elimination based on context length, support and cardinality parameters. The performed evaluation tests show that using the proposed optimization techniques, we can reduce the average inflection time to 0.52

Keywords

MSC

68T50

Cite this article

@article{cai-4645,
  author  = {Gábor Szabó and László Kovács},
  title   = {Optimization of the Morpher Morphology Engine Using Knowledge Base Reduction Techniques},
  journal = {Computing and Informatics},
  volume  = {38},
  number  = {4},
  pages   = {963--985},
  year    = {2019},
  issn    = {1335-9150},
  doi     = {10.31577/cai_2019_4_963}
}
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