Unified low sample relation extraction method and device based on multi-choice matching network
A technology of matching network and relation extraction, applied in neural learning methods, biological neural network models, unstructured text data retrieval, etc. Calculation cost and calculation speed, and the effect of improving model performance
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[0066] For the relationship extraction task containing the following three relationship categories: "employee", "employer", "investor", the example to be classified is: "Cook is the CEO of Apple", and it is processed correspondingly, and Relation extraction is performed through a multi-choice matching network.
[0067] Implementation:
[0068] (1) A description of all target relationships of the current task. Concatenated into multiple-choice statements:
[0069] [choice] employee [choice] employer [choice] investor [choice] other
[0070] (2) Splicing the instance to be classified with the multiple-choice statement:
[0071] [choice]employee[choice]employer[choice]investor[choice]other[sep][e1]Cook[ / e1] is the CEO of [e2]Apple[ / e2]
[0072] (3) Input the above processed results into the multi-selection matching network, and calculate the similarity between the instance to be classified and each category, and the most similar relationship is the prediction result. In this...
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