Named entity identification method for medical text data
A named entity recognition and text data technology, applied in the field of information extraction, can solve the problems of medical named entity recognition of medical text data, etc.
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[0024] In this embodiment, Hidden Markov Model (HMM) is used to perform sequence labeling on the original medical text to obtain the predicted word segmentation result. After the prediction of word segmentation, the semi-supervised learning method is used to perform iterative self-learning on the word segmentation results to obtain accurate word segmentation and named entity recognition results. In this embodiment, by comparing the advantages and disadvantages of various supervised learning methods and combining semi-supervised learning methods with error correction, the research on longitudinal named entity recognition of disease types. The aim is to summarize methods that can extract accurate information quickly and with little manual intervention.
[0025] Use HMM to solve named entity recognition and annotation, that is, given a sequence of observations (1):
[0026] P(Y|X)=p(x 1 , N), X={x 1 , X 2 ,...X n } (1)
[0027] To find an optimal mark sequence (2) that...
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