A Biologically Motivated and Computationally Efficient Natural Language Processor

Abstract. Conventional artificial neural network models lack many physiological properties of the neuron. Current learning algorithms are more concerned to computational performance than to biological credibility. Regarding a natural language processing application, the thematic role assignment – semantic relations between words in a sentence –, the purpose of the proposed system is to compare two different connectionist modules for the same application: (1) the usual simple recurrent network using backpropagation learning algorithm with (2) a biologically inspired module, which employs a bi-directional architecture and learning algorithm more adjusted to physiological attributes of the cerebral cortex. Identical sets of sentences are used to train the modules. After training, the achieved output data show that the physiologically plausible module displays higher accuracy for expectable thematic roles than the traditional one.