Deep learning with sentence embeddings pre-trained on biomedical corpora improves the performance of finding similar sentences in electronic medical records

摘要

An overview of our models. The Random Forest uses manually crafted features (word tokens, character n-grams, sequence similarity, semantic similarity and named entities). The feature selection of the Random Forest was done on the validation set. The Neural Network uses vectors generated by sentence embeddings as inputs. The validation set was used to monitor the early stopping process of the neural network. The ensembled (stacking) model incorporates both the Random Forest and Neural Network models. The validation set was used to train the ensembled model