Address cutting method, device and equipment and readable storage medium
An address and address tree technology, applied in the field of semantic analysis, can solve problems such as address cutting efficiency decline, address cutting irregularities, address cutting template cutting errors, etc., to achieve efficient address cutting, reduce labeling errors, and accurate results
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Embodiment 1
[0046] The embodiment of the present invention provides a method for cutting addresses, such as figure 1 As shown, the method specifically includes the following steps:
[0047] Step S101: Obtain the address text to be processed, and obtain a pre-trained address segmentation model; wherein, the address segmentation model includes: a BERT algorithm layer and a CRF algorithm layer.
[0048] Wherein, the address text contains several words representing address elements, and address elements are the constituent units of an address, for example: xx province, xx city, xx district, xx street, etc. are all address elements.
[0049] The address segmentation model is a model trained based on a large amount of address sample data that can be used to identify address elements in text, and the address segmentation model is composed of a BERT algorithm layer and a CRF algorithm layer.
[0050] BERT (Bidirectional Encoder Representation from Transformers, deep bidirectional pre-training pr...
Embodiment 2
[0105] The embodiment of the present invention provides a device for cutting addresses, such as figure 2 As shown, the device specifically includes the following components:
[0106] The obtaining module 201 is used to obtain the address text to be processed, and obtain a pre-trained address segmentation model; wherein, the address segmentation model includes: a BERT algorithm layer and a CRF algorithm layer;
[0107] The labeling module 202 is used to add a corresponding label for each word in the address text by using the BERT algorithm layer; wherein, the label is used to indicate whether the word in the address text belongs to an address element;
[0108] Calibration module 203, for using the CRF algorithm layer to calculate the label accuracy rate according to the label corresponding to each word in the address text;
[0109] The determining module 204 is configured to determine the address elements in the address text according to the annotation corresponding to each w...
Embodiment 3
[0139] This embodiment also provides a computer device, such as a smart phone, a tablet computer, a notebook computer, a desktop computer, a rack server, a blade server, a tower server, or a cabinet server (including an independent server, or A server cluster composed of multiple servers), etc. Such as image 3 As shown, the computer device 30 in this embodiment at least includes but is not limited to: a memory 301 and a processor 302 that can be communicated with each other through a system bus. It should be pointed out that, image 3 Only computer device 30 is shown having components 301-302, but it should be understood that implementing all of the illustrated components is not a requirement and that more or fewer components may instead be implemented.
[0140] In this embodiment, the memory 301 (that is, a readable storage medium) includes a flash memory, a hard disk, a multimedia card, a card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), s...
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