Semantic interpretation and the resolution of ambiguity
Semantic interpretation and the resolution of ambiguity
A connectionist approach to word sense disambiguation
A connectionist approach to word sense disambiguation
Distributed Representations, Simple Recurrent Networks, And Grammatical Structure
Machine Learning - Connectionist approaches to language learning
Symbolic knowledge and neural networks: insertion, refinement and extraction
Symbolic knowledge and neural networks: insertion, refinement and extraction
A parallel distributed approach for knowledge-based inference and learning
Hybrid architectures for intelligent systems
Knowledge-based artificial neural networks
Artificial Intelligence
Hybrid Neural Network and Expert Systems
Hybrid Neural Network and Expert Systems
In-Depth Understanding: A Computer Model of Integrated Processing for Narrative Comprehension
In-Depth Understanding: A Computer Model of Integrated Processing for Narrative Comprehension
Neural Networks in Computer Intelligence
Neural Networks in Computer Intelligence
Intelligent Hybrid Systems
Computational Architectures Integrating Neural and Symbolic Processes: A Perspective on the State of the Art
Artificial Intelligence and Neural Networks; Steps toward Principled Integration
Artificial Intelligence and Neural Networks; Steps toward Principled Integration
Towards a dictionary support environment for real time parsing
EACL '85 Proceedings of the second conference on European chapter of the Association for Computational Linguistics
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Connectionist methods and knowledge-based techniques are two largely complementary approaches to natural language processing (NLP). However, they both have some potential problems which preclude their being a general purpose processing method. Research reveals that a hybrid processing approach that combines connectionist with symbolic techniques may be able to use the strengths of one processing paradigm to address the weakness of the other one. Hence, a system that effectively combines the two different approaches can be superior to either one in isolation. This paper describes a hybrid system—SYMCON (SYMbolic and CONnectionist) which integrates symbolic and connectionist techniques in an attempt to solve the problem of word sense disambiguation (WSD), which is arguably one of the most fundamental and difficult issues in NLP. It consists of three sub-systems: first, a distributed simple recurrent network (SRN) is trained by using the standard back-propagation algorithm to learn the semantic relationships among concepts, thereby generating categorical constraints that are supplied to the other two sub-systems as the initial results of pre-processing. The second sub-system of SYMCON is a knowledge-based symbolic component consisting of a knowledge base containing general inferencing rules in a certain application domain. Third, a localist network is used to select the best interpretation among multiple alternatives and potentially ambiguous inference paths by spreading activation throughout the network. The structure, initial states, and connection weights of the network are determined by the processing outcome in the other two sub-systems. This localist network can be viewed as a medium between the distributed network and the symbolic sub-system. Such a hybrid symbolic/connectionist system combines information from all three sources to select the most plausible interpretation for ambiguous words.