DocumentCode :
295871
Title :
Rapid best-first retrieval from massive dictionaries by lazy evaluation of a syntactic neural network
Author :
Lucas, S.M.
Author_Institution :
Dept. of Electron. Syst. Eng., Essex Univ., Colchester, UK
Volume :
5
fYear :
1995
fDate :
Nov/Dec 1995
Firstpage :
2237
Abstract :
A new method of searching large dictionaries given uncertain inputs is described, based on the lazy evaluation of a syntactic neural network (SNN). The new method is shown to significantly outperform a conventional trie-based method for large dictionaries (e.g. in excess of 100,000 entries). Results are presented for the problem of recognising UK postcodes using dictionary sizes of up to 1 million entries. Most significantly, it is demonstrated that the SNN actually gets faster as more data is loaded into it
Keywords :
character recognition; neural nets; UK postcodes; lazy evaluation; massive dictionaries; rapid best-first retrieval; syntactic neural network; Associative memory; Character recognition; Content based retrieval; Dictionaries; Hidden Markov models; Information retrieval; Neural networks; Performance evaluation; Speech recognition; Systems engineering and theory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2768-3
Type :
conf
DOI :
10.1109/ICNN.1995.487709
Filename :
487709
Link To Document :
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