Extracting product purchase information from electronic messages

摘要

Improved systems and methods for extracting product purchase information from electronic messages transmitted between physical network nodes to convey product purchase information to designated recipients. These examples provide a product purchase information extraction service that is able to extract product purchase information from electronic messages with high precision across a wide variety of electronic message formats and thereby solve the practical problems that have arisen as a result of the proliferation of different electronic message formats used by individual merchants and across different merchants and different languages. In this regard, these examples are able to automatically learn the structures and semantics of different message formats, which accelerates the ability to support new message sources, new markets, and different languages.

主权项

1. A computer-implemented method, comprising:
for each purchase-related electronic message in a group of purchase-related electronic messages selected from a collection of electronic messages transmitted between network nodes and stored in a first networked non-transitory computer-readable memory,
segmenting, by a processor, contents of the electronic message into tokens;matching the electronic message to one of multiple clusters of purchase-related electronic messages, wherein each cluster is associated with a respective grammar that recursively defines a respective allowable arrangement of tokens corresponding to structural elements of the electronic messages in the matched cluster;parsing, by a processor, the tokens segmented from the electronic message in accordance with the grammar associated with the cluster matched to the electronic message, wherein the parsing comprises identifying the tokens segmented from the electronic message that correspond to respective structural elements defined in the grammar and extracting unidentified tokens segmented from the electronic message as field tokens;determining classification features from the tokens corresponding to structural elements of the electronic messages in the matched cluster;classifying, by at least one machine learning classifier, the extracted field tokens with respective product purchase relevant labels based on the determined classification features; storing associations between the product purchase relevant labels and the respective extracted field tokens as aggregated data in a second networked non-transitory computer-readable memory; and transmitting data for displaying a view based on the aggregated data on a client network node.