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

机译:句法神经网络延迟评估通过巨大评估从大规模词典中快速最佳检索

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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)的速度评估,描述了给出给出不确定输入的新词典的新方法。显示新方法显着优于大字典的传统基于Trie的方法(例如,超过100,000个条目)。使用高达100万条目的字典大小识别英国邮政编码的问题提出了结果。最重要的是,据证明SNN实际上会随着更多数据加载到其中而变得更快。

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