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The traditional named entity disambiguation technology usually relies on rich context and knowledge of external entities. However, many emerging entities lack knowledge bases and the text containing entities is short. These limitations make traditional algorithms unable to make full use of contextual semantic information. At the same time,due to the limitation of the number of effective samples, the final application scenarios of the algorithm are very limited. Based on the above defects, this paper proposes a deep learning-based entity disambiguation method combining bidirectional encoder representation from transformers(BERT) model and long short-term memory neural network.The main work are the following parts: 1) A word vector based on the BERT model is designed to obtain more information through fewer data samples. 2) In order to allow the long short-term memory neural networks to retain useful information and verify that the short text applies to the method of this article, this method segments the sentence samples. 3) This article uses the neural network intelligence(NNI) technology proposed by Microsoft, which makes it possible to quickly and efficiently obtain the optimal neural network hyperparameter. This study compares other different types of word vectors and neural network technology, confirming that the F-Measure value of the entity disambiguation technology based on deep learning used in this paper is higher.

Focusing on the sampling representative and the uniformity of sample processing, the influence of processing grain size of gold and silver in the lead-bismuth alloy on the gold and silver grade, and the distribution and regularity of gold and silver in the product ingot were studied. The sampling, sample processing and laboratory analysis schemes of lead-bismuth alloys were developed by testing the distribution of gold and silver in lead-bismuth alloys. [ABSTRACT FROM AUTHOR]