Abstract: Embodiments disclosed herein provide automated account recommendations for a hierarchical account structure. For an incoming transaction data record, a first language model is used to generate a recommended account name that is agnostic to the existing list of accounts. The recommended account name is appended to the existing list of accounts, which is consolidated to remove synonymous accounts. Additionally, a hierarchical relationship between the different accounts in the consolidated list of accounts is determined. A second language model is used to select an account name from the list of accounts. The selected account name along with any hierarchically related account name may be displayed to the user for selection.

Abstract: A method extracts explainable corpora embeddings. The method includes constructing a graph with nodes representing terms from a text sequence and edges that include pointwise values generated between pairs of terms. The method further includes generating a rank vector from the graph. Elements of the rank vector correspond to the edges of the graph. A rank value, of the rank values, corresponds to a term from the text sequence. The method further includes selecting the term by comparing the rank vector to a previous rank vector generated for a previous text sequence.

Abstract: A method extracts explainable corpora embeddings. The method includes constructing a graph with nodes representing terms from a text sequence and edges that include pointwise values generated between pairs of terms. The method further includes generating a rank vector from the graph. Elements of the rank vector correspond to the edges of the graph. A rank value, of the rank values, corresponds to a term from the text sequence. The method further includes selecting the term by comparing the rank vector to a previous rank vector generated for a previous text sequence.