Abstract. 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 Morpho 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. 1

Work in morphology learning has thus far been primarily divided into two lines of research: cognitively-motivated models of morphology learning, which attempt to model human development and competency, and engineering-oriented models, which attempt to maximize application performance. In this paper we

We use the Base and Transforms Model proposed by Chan [1] as the core of a morphological analyzer, extending its concept of base-derived relationships to allow multi-step derivations and adding a number of features required for robustness on larger corpora. The result is a rule-based morphological analyzer, attaining an F-score of 58.48 % in English and 33.61 % in German in the Morphochallenge 2009 Competition 1 evaluation.

We propose a statistical test for measuring grammatical productivity. We show that very young children’s knowledge is consistent with a systematic grammar that independently combines linguistic units. To a testable extent, the usage-based approach to language and language learning, which emphasizes the role of lexically specific memorization, is inconsistent with the child language data. We also discuss the connection of this research with developments in computational and theoretical linguistics. 1