Abstract: The disclosure herein describes training a document recommendation model using loss data generated from a linear score difference vector. A training data entry is provided including a query and a set of candidate documents. A document recommendation model generates a set of document prediction scores indicative of a likelihood that the candidate documents are responses to the query and a pairwise score difference matrix is generated using the set of document prediction scores. The pairwise score difference matrix is transformed into a score difference vector using a correct document vector that indicates a correct document among the set of candidate documents. Loss data of the document recommendation model is generated using the score difference vector and the document recommendation model is adjusted using the calculated loss data. Training the document recommendation model based on the linear score difference vector reduces resource usage when compared to training with a difference matrix.

Abstract: Systems and methods are providing for associating portions of data from a first data file to a second data file. The association may be used to generate machine learning libraries or for other purposes. Exemplary embodiments may include a first data file of a text extraction of a dialog between a clinician and a patient and the second data file are clinical notes obtained from the exchange between the clinician and the patient.