Title :
A modular connectionist parser for resolution of pronominal anaphoric references in multiple sentences
Author :
de Oliverira, I.L. ; Wazlawick, Raul Sidnei
Author_Institution :
Univ. of West of Santa Catarina, Chapeco, Brazil
Abstract :
A connectionist model used in the resolution of a well-known linguistic phenomenon, the pronominal anaphoric reference, is presented. The model is composed of two neural networks: a simple recurrent neural network (parser) and a feedforward neural network (segmenter). These networks are trained and tested simultaneously. With this model it is possible to solve anaphoric references with text segments of arbitrary size, that is to say, with any number of sentences
Keywords :
computational linguistics; feedforward neural nets; natural languages; recurrent neural nets; feedforward neural network; modular connectionist parser; multiple sentences; pronominal anaphoric reference resolution; recurrent neural network; text segments; Cats; Dogs; Feedforward neural networks; Helium; Humans; Intelligent networks; Neural networks; Recurrent neural networks; Testing;
Conference_Titel :
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
0-7803-4859-1
DOI :
10.1109/IJCNN.1998.685943
