Hybrid Connectionist Natural Language Processing

The objective of this book is to describe a new approach in hybrid connectionist natural language processing which bridges the gap between strictly symbolic and connectionist systems. This objective is tackled in two ways: the book gives an overview of hybrid connectionist architectures for natural language processing; and it demonstrates that a hybrid connectionist architecture can be used for learning real-world natural language problems. The book is primarily intended for scientists and students interested in the fields of articial intelligence, neural networks, connectionism, natural language processing, hybrid symbolic connectionist architectures, parallel distributed processing, machine learning, automatic knowledge acquisition or computational linguistics. Furthermore, it might be of interest for scientists and students in information retrieval and cognitive science, since the book points out interdisciplinary relationships to these fields. We develop a systematic spectrum of hybrid connectionist architectures, from completely symbolic architectures to separated hybrid connectionist architectures, integrated hybrid connectionist architectures and completely connectionist architectures. Within this systematic spectrum we have designed a system SCAN with two separated hybrid connectionist architectures and two integrated hybrid connectionist architectures for a scanning understanding of phrases. A scanning understanding is a relation-based at understanding in contrast to traditional symbolic in-depth understanding. Hybrid connectionist representations consist of either a combination of connectionist and symbolic representations or dierent connectionist representations. In particular, we focus on important tasks like structural disambiguation and semantic context classication. We show that a parallel modular, constraint-based, plausibility-based and learned use of multiple hybrid connectionist representations provides powerful architectures for learning a scanning understanding. In particular, the combination of direct encoding of domain-independent structural knowledge and the connectionist learning of domain-dependent semantic knowledge, as suggested by a scanning understanding in SCAN, provides concepts which lead to exible, adaptable, transportable architectures for different domains.

@Book{Wer95a, 
 	 author =  {Wermter, Stefan},  
 	 title = {Hybrid Connectionist Natural Language Processing}, 
 	 number = {},
 	 volume = {},
 	 year = {1995},
 	 month = {},
 	 publisher = {Chapman and Hall},
 	 doi = {}, 
 }