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Rapid best-first retrieval from massive dictionaries by lazy evaluation of a syntactic neural network

机译:通过语法神经网络的惰性评估,从海量词典中快速进行最佳优先检索

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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.
机译:基于句法神经网络(SNN)的惰性评估,描述了一种在给定不确定输入的情况下搜索大词典的新方法。对于大型词典(例如,超过100,000个条目),该新方法显示出明显优于传统的基于Trie的方法。给出了针对使用多达100万个条目的词典大小来识别英国邮政编码的问题的结果。最重要的是,事实证明,随着向其中加载更多数据,SNN实际上会变得更快。

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