A Rule-Based Acquisition Model Adapted for Morphological Analysis

We adapt the cognitively-oriented morphology acquisition model proposed in (Chan 2008) to perform morphological analysis, extending its concept of base-derived relationships to allow multi-step derivations and adding features required for robustness on noisy corpora. This results in a rule-based morphological analyzer which attains an F-score of 58.48% in English and 33.61% in German in the Morph Challenge 2009 Competition 1 evaluation. The learner's performance shows that acquisition models can effectively be used in text-processing tasks traditionally dominated by statistical approaches.