Semantic processing of English sentences using statistical computation based on neurophysiological models.

Marcia T Mitchell

doi:10.3389/fphys.2015.00135

Abstract

Computer programs that can accurately interpret natural human language and carry out instructions would improve the lives of people with language processing deficits and greatly benefit society in general. von Neumann in theorized that the human brain utilizes its own unique statistical neuronal computation to decode language and that this produces specific patterns of neuronal activity. This paper extends von Neumann's theory to the processing of partial semantics of declarative sentences. I developed semantic neuronal network models that emulate key features of cortical language processing and accurately compute partial semantics of English sentences. The method of computation implements the MAYA Semantic Technique, a mathematical technique I previously developed to determine partial semantics of sentences within a natural language processing program. Here I further simplified the technique by grouping repeating patterns into fewer categories. Unlike other natural language programs, my approach computes three partial semantics. The results of this research show that the computation of partial semantics of a sentence uses both feedforward and feedback projection which suggest that the partial semantic presented in this research might be a conscious activity within the human brain.

Highlights

Determining how the human brain processes the meaning of language could be important in helping people with deficits in language comprehension, either because of specific brain disorders dementia or brain lesions (Ullman, 2001; Cooke et al, 2003; Dronkers et al, 2004; Schirmer, 2004; Awad et al, 2007; Sonty et al, 2007; Christensen, 2008; Pulvermüller and Fadiga, 2010; Wright et al, 2012)
I extended this idea to develop a semantic neuronal network model that uses a mathematical language to determine the partial semantics of sentences
The default values from the Simulator for Neuronal Networks and Action Potentials (SNNAP) software from the University of Texas Health Science Center was used for the network

Summary

Introduction

Determining how the human brain processes the meaning of language could be important in helping people with deficits in language comprehension, either because of specific brain disorders dementia or brain lesions (Ullman, 2001; Cooke et al, 2003; Dronkers et al, 2004; Schirmer, 2004; Awad et al, 2007; Sonty et al, 2007; Christensen, 2008; Pulvermüller and Fadiga, 2010; Wright et al, 2012) These insights can inform the development of innovative artificial intelligence that understands and carries out instructions from humans (Pollack, 2005; Russell and Norvig, 2009). Sentence meaning is derived from syntactic structure and is dependent on the syntax for its combinatorial properties (Kuperberg, 2007, p. 24)

Methods

Results

Conclusion