Conventional and associative memory-based spelling checkers

The authors review conventional and emerging neural approaches to fault-tolerant data retrieval when the input keyword and/or database itself may contain noise (errors). Spelling checking is used as a primary example to illustrate various approaches and to contrast the difference between conventional (algorithmic) techniques and research methods based on neural associative memories. Recent research on associative spelling checkers is summarized and some original results are presented. It is concluded that most neural models do not provide a viable solution for robust data retrieval, due to saturation and scaling problems. However, a combination of conventional and neural approaches is shown to have excellent error correction rates and low computational costs; hence, it can be a good choice for robust data retrieval in large databases.