A neural network model for acquisition of semantic structures

Author

Chan, Samuel W K ; Franklin, James

Author_Institution

Sch. of Comput. Sci. & Eng., New South Wales Univ., NSW, Australia

fYear

1994

fDate

13-16 Apr 1994

Firstpage

221

Abstract

Research suggests that natural language processing (NLP) can be profitably viewed in terms of the spread of activation through a neural network. However, since the critique by Fodor (1988) of the style of connectionist representations, one of the biggest challenges facing proponents of connectionist models of NLP is the rich structures of language. As models of NLP, neural network systems must exhibit the properties of compositionality and structure sensitivity. This paper describes a neural network model in which simple recurrent network and recursive auto-association memory are combined to acquire the semantic structures from sentence constituents. This imposes no prior limit on sentence structures. The model can be viewed as a tool of conceptual acquisition and generalization extraction in language understanding

Keywords

grammars; learning (artificial intelligence); natural languages; recurrent neural nets; speech analysis and processing; speech recognition; connectionist models; connectionist representations; language understanding; natural language processing; neural network model; neural network systems; recurrent network; recursive auto-association memory; semantic structures acquisition; sentence constituents; Artificial neural networks; Australia; Biological neural networks; Computer science; Hardware; Humans; Mathematics; Natural language processing; Neural networks; Recurrent neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Speech, Image Processing and Neural Networks, 1994. Proceedings, ISSIPNN '94., 1994 International Symposium on

Print_ISBN

0-7803-1865-X

Type

conf

DOI

10.1109/SIPNN.1994.344927

Filename

344927