Improved hierarchical sequence labeling joint relationship extraction method based on neural network
A technology for hierarchical sequence and relation extraction, applied in biological neural network models, neural architectures, instruments, etc., to alleviate the problem of entity nesting and achieve accurate entity extraction.
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Embodiment 1
[0039] An improved hierarchical sequence annotation joint relation extraction method based on neural network, see figure 2 , the method includes the following steps:
[0040] 101: Input the text into the model, and obtain the text feature vector output through the pre-training model BERT.
[0041] 102: Decode the text feature vector through a convolutional neural network module (Convolutional Neural Networks, CNN), and output the subject's head position mark sequence.
[0042] It can be noticed that the present invention uses a well-designed CNN module followed by the pre-training model BERT for sequence labeling. Most of the existing methods for sequence labeling models are very simple and have limited ability to fuse contextual information. This method effectively utilizes the advantage that the CNN module pays more attention to local information, and can also supplement more location features to a certain extent, so as to perform more accurate annotation.
[0043] 103: ...
Embodiment 2
[0049] The scheme in embodiment 1 is further introduced below in conjunction with specific examples and calculation formulas, see the following description for details:
[0050] 201: Input the text into the model, and obtain the text feature vector through the pre-training model BERT.
[0051] Among them, the above step 201 mainly includes: preprocessing the input text, truncating or supplementing it according to the specified length n, inputting BERT, and according to the BERT word list, each word has its corresponding ID, so the corresponding ID of the text sequence can be obtained. ID sequence, the length is n. Then input the ID sequence into the BERT model to obtain the output text feature vector where n represents the length of the text and k represents the dimension of the text feature vector for each word.
[0052] 202: Convert the text feature vector Decoding by a CNN module outputs a sequence of head position markers for the subject.
[0053] Wherein the CNN mod...
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