Biologically Plausible Connectionist Prediction of Natural Language Thematic Relations

In Natural Language Processing (NLP) symbolic systems, several linguistic phenomena, for instance, the thematic role relationships between sentence constituents, such as AGENT, PATIENT, and LOCATION, can be accounted for by the employment of a rule-based grammar. Another approach to NLP concerns the use of the connectionist model, which has the benefits of learning, generalization and fault tolerance, among others. A third option merges the two previous approaches into a hybrid one: a symbolic thematic theory is used to supply the connectionist network with initial knowledge. Inspired on neuroscience, it is proposed a symbolic-connectionist hybrid system called BIO theta PRED (BIOlogically plausible thematic (theta) symbolic-connectionist PREDictor), designed to reveal the thematic grid assigned to a sentence. Its connectionist architecture comprises, as input, a featural representation of the words (based on the verb/noun WordNet classification and on the classical semantic microfeature representation), and, as output, the thematic grid assigned to the sentence. BIO theta PRED is designed to ""predict"" thematic (semantic) roles assigned to words in a sentence context, employing biologically inspired training algorithm and architecture, and adopting a psycholinguistic view of thematic theory.Fapesp - Fundacao de Amparo a Pesquisa do Estado de Sao Paulo, Brazil[2008/08245-4

Biological and Cognitive Plausibility in Connectionist Networks for Language Modeling

If we want to explain cognitive processes with means of connectionist networks, these networks have to correspond with cognitive systems and their underlying biological mechanisms in different respects. The question of biological and cognitive plausibility of connectionist models arises from two different aspects – first, from the aspect of biology – on one hand, one has to have a fair understanding of biological mechanisms and cognitive mechanisms in order to represent them in a model, and on the other hand there is the aspect of modeling – one has to know how to construct a model to represent precisely what we are aiming at. Computer power and modeling techniques have improved dramatically in recent 20 years, so the plausibility problem is being addressed in more adequate ways as well. Connectionist models are often used for representing different aspects of natural language. Their biological plausibility had sometimes been questioned in the past. Today, the field of computational neuroscience offers several acceptable possibilities of modeling higher cognitive functions, and language is among them. This paper brings a presentation of some existing connectionist networks modeling natural language. The question of their explanatory power and plausibility in terms of biological and cognitive systems they are representing is discussed