CPC G06F 40/295 (2020.01) [G06F 40/10 (2020.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 30/153 (2022.01); G06V 30/262 (2022.01); G06V 30/40 (2022.01); G16H 15/00 (2018.01); G06N 3/08 (2013.01); G06V 30/10 (2022.01)] | 20 Claims |
1. A natural language understanding method, the method comprising:
obtaining a radiological report text, the radiological report text containing one or more clinical findings;
in response to detecting one or more errors in the radiological report text, substituting a best replacement candidate for each of the one or more errors, the best replacement candidate calculated by analyzing a plurality of character-level optical transformation costs weighted by a frequency analysis over a corpus corresponding to the radiological report text;
for each given word within the radiological report text:
obtaining a word embedding;
determining a plurality of character-level embeddings; and
concatenating the word embedding and the plurality of character-level embeddings to a neural network;
generating, using the neural network, a plurality of NER tagged spans for the radiological report text, the generating based on the concatenated word and character-level embedding s;
calculating a set of linked relationships for the plurality of NER tagged spans by:
generating one or more masked text sequences based on the radiological report text and determined pairs of potentially linked NER spans;
calculating a dense adjacency matrix based on attention weights obtained from providing the one or more masked text sequences to a Transformer deep learning network; and
performing graph convolutions over the calculated dense adjacency matrix via a graph convolutional network.
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