Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Evaluation of an inference network-based retrieval model
ACM Transactions on Information Systems (TOIS) - Special issue on research and development in information retrieval
Transition network grammars for natural language analysis
Communications of the ACM
Knowledge-Based and Statistical Approaches to Text Retrieval
IEEE Expert: Intelligent Systems and Their Applications
Generating, integrating, and activating thesauri for concept-based document retrieval
IEEE Expert: Intelligent Systems and Their Applications
A stochastic parts program and noun phrase parser for unrestricted text
ANLC '88 Proceedings of the second conference on Applied natural language processing
Development of a name translation system using CRAY T94
HPC-ASIA '97 Proceedings of the High-Performance Computing on the Information Superhighway, HPC-Asia '97
A hybrid approach to fuzzy name search incorporating language-based and text-based principles
Journal of Information Science
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Accuracy is critical when multiple databases are merged into a single system, because an error in a single record could lead to multiple mismatches. Address normalization is fairly common in database merging. We have developed a system to accurately and efficiently normalize mailing addresses. However, our system differs from other neural network architectures. Its key ingredients are an address dictionary and a scoring system. The scoring system is based on analog neural network systems, but the address dictionary follows a digital approach. The two key processes in our system are learning and address normalization. Learning is further split into dictionary creation updating and system parameters training.