Computational complexity of abstractive morphology

Abstractive and constructive approaches to word structure make radically different assumptions concerning nature and role of the building blocks that make up a speaker's morphological competence. In this contribution, we show that the two views are also computationally different. In particular, we contend that a number of problems arising in connection with a subsymbolic implementation of the constructive view (as epitomised by classical multi-layered perceptrons) are tackled effectively, or disappear altogether, in a neurally-inspired implementation of associative networks, resting on key-notions such as self-organization and emergence. A particular variant of Kohonen's Self-Organizing Map is introduced as a model to explore and assess the implications of an abstractive approach in terms of its computational complexity. Details of the model (Temporal Self-Organizing Map, TSOM) and experimental data are shown to illustrate the interplay between processing and storage in language acquisition.