Naming entity identification method

Abstract

The invention relates to a naming entity identification method, belonging to the information technology field. Firstly, a named entity recognition corpus is established to train the named entity recognition model based on LSTM neural network. And then segmenting the text data to be recognized; Secondly, the CRF model is used to recognize the text data of the divided words. Finally, the trained named entity recognition model is used to recognize the place name and organization name, and the final result of named entity recognition is obtained by de-duplication of person name. By introducing theLSTM neural network, the invention solves the phenomenon that a single named entity recognition technology based on a statistical model is not accurate enough to recognize the boundary, and the recognition rate of new words is low, so that the recognition result of the named entity is low in accuracy, so as to improve the accuracy of the named entity recognition.

Description

technical field [0001] The invention relates to a named entity recognition method, which belongs to the field of information technology. Background technique [0002] With the rapid development of the Internet and the information industry, massive text data is constantly being generated. How to efficiently obtain useful information from massive text data has become a research hotspot. Information extraction technology has emerged as the times require, and named entity recognition is information extraction. A subtask of , whose purpose is to extract specified entities from massive text data. In the field of natural language processing applications, named entity recognition is the basic task of many natural language processing applications such as information retrieval, machine translation, and sentiment analysis. Therefore, its research is of great significance and value. [0003] Generally, there are various types of named entities and a large number, and new named entities...

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