Entity identification method, device, device and storage medium

A technology for entity identification and storage medium

Active Publication Date: 2019-02-01
GUANGZHOU DUOYI NETWORK TECH +2
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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 e

Method used

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  • Entity identification method, device, device and storage medium
  • Entity identification method, device, device and storage medium
  • Entity identification method, device, device and storage medium

Examples

Experimental program
Comparison scheme
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Embodiment 1

[0053] See figure 1 , figure 1 is a schematic diagram of the entity recognition device provided in Embodiment 1 of the present invention, which is used to execute the entity recognition method provided in the embodiment of the present invention, such as figure 1 As shown, the entity recognition device includes: at least one processor 11, such as CPU, at least one network interface 14 or other user interface 13, memory 15, at least one communication bus 12, and the communication bus 12 is used to realize the connection between these components communication. Wherein, the user interface 13 may optionally include a USB interface, other standard interfaces, and a wired interface. The network interface 14 may optionally include a Wi-Fi interface and other wireless interfaces. The memory 15 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory. The memory 15 may optionally include at least one stor...

Embodiment 2

[0063] figure 2 It is a schematic flowchart of an entity recognition method provided in Embodiment 2 of the present invention.

[0064] A method for entity recognition, comprising the steps of:

[0065] S11. Obtain the LSTM-based entity recognition model after training, wherein the LSTM-based entity recognition model is trained using labeled training corpus;

[0066] S12. Input the text to be recognized into the LSTM-based entity recognition model after the training is completed, and obtain the probability that each character in the text to be recognized belongs to a labeled label;

[0067] S13. Input the probability into the CRF model to obtain the marks of each character.

[0068] In the embodiment of the present invention, in order to improve the accuracy and efficiency of entity recognition, the LSTM model and the CRF model are combined to realize entity recognition and sentence entity recognition at the same time.

[0069] Preferably, the acquired LSTM-based entity re...

Embodiment 3

[0095] see Image 6 , a schematic structural diagram of an entity recognition device provided in a third embodiment of the present invention;

[0096] An entity recognition device, comprising:

[0097] Entity recognition model acquisition module 31 is used to obtain the LSTM-based entity recognition model after the training is completed, wherein the LSTM-based entity recognition model is trained using the labeled training corpus;

[0098] The probability acquisition module 32 is used to input the text to be recognized into the LSTM-based entity recognition model after the training is completed, and obtain the probability that each character in the text to be recognized by the entity belongs to the tag label;

[0099] A mark acquisition module 33, configured to input the probability into the CRF model to obtain the mark of each character.

[0100] Preferably, the entity recognition model acquisition module 31 includes:

[0101] The training corpus acquisition unit is used to o...

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PUM

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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...

Claims

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Application Information

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IPC IPC(8): G06F17/27G06N3/08
CPCG06N3/08G06F40/295G06F40/289
Inventor 徐波
Owner GUANGZHOU DUOYI NETWORK TECH
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