Entity identification method, device, device and storage medium

Abstract

The invention discloses an entity identification method, which comprises the following steps: obtaining an LSTM-based entity identification model after training is completed; wherein, the LSTM-based entity identification model is trained by using the annotated training corpus; the LSTM-based entity identification model is trained by using the annotated training corpus. Inputting the text to be identified into the LSTM-based entity recognition model after the training is completed, and obtaining the probability that each character in the text to be identified belongs to a label label; Inputtingthe probability into the CRF model to obtain marks of each character; LSTM networks rely heavily on data, The size and quality of the data will also affect the training results of the model, Combining LSTM model and CRF model, The LSTM model is used to solve the problem of extracting sequence features, and the CRF model can effectively utilize the sentence-level markup information. The LSTM + CRFmodel improves the execution efficiency of the dialogue system, and at the same time, the entity recognition and word segmentation are realized, which improves the accuracy and efficiency of entity recognition.

Description

technical field [0001] The present invention relates to the field of information technology, in particular to an entity recognition method, device, equipment and storage medium. Background technique [0002] In the field of artificial intelligence, attempts to mimic a human's ability to converse can be traced back to the early days of artificial intelligence. In the past few years, messaging service applications have grown rapidly. Domestic WeChat, foreign WhatsApp, Facebook Messenger, etc., have almost occupied all fragmented time of users, with hundreds of millions of active users, and have in fact become mobile Internet era. At the "browser" entrance, users only need to use one application to obtain most of the information. The traffic bonus brought by downloading mobile applications is slowly disappearing. This is reflected in the advantages of the dialogue system. The development cost is low, and it can Attached to the software platform. [0003] In the dialogue syste...

AI Technical Summary

Problems solved by technology

However, the two tasks of entity recognition and word segmentation in existing dialogue systems are processed separately.

[0004] When the inventor implements the present invention, he finds that there are deficiencies in the application of entity recognition in the prior art: entity recognition is to identify the entity words in it from the sentence level, such as: the name of a person , place name, organization name

It is similar to word segmentation. If these two tasks are performed in isolation, the accuracy of entity word recognition and word segmentation will decrease, such as the sentence: Nanjing Yangtze River Bridge

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