Hierarchical classification of transaction data

A system comprising:

• one or more computers;
• and
• one or more storage devices which store instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:
• receiving labeled transaction records as training data, the transaction records including token sequences;
• forming, from a set of tags each representing a respective different aspect of the transaction records, tag-level sequence data from the token sequences, the tag-level sequence data including tag sequences corresponding to the token sequences, wherein for each tag sequence, each tag in the tag sequences is an abstraction of one or more respective tokens of the corresponding token sequence;
• clustering the tag-level sequence data into a plurality of tag sequence clusters based on distances between the tag sequences, wherein the distances between tag sequences are determined at least in part based on an ordering of the tags in the tag sequences;
• training a cluster-level classifier that predicts mappings between i) the transaction records and ii) the tag sequence clusters;
• training a plurality of cluster-specific classifiers, wherein:
• each cluster-specific classifier corresponds to a respective tag sequence cluster, and
• each cluster-specific classifier corresponding to a respective tag sequence cluster predicts mappings between i) transaction records that have been classified by the cluster-level classifier as belonging to the respective tag sequence cluster and ii) one or more labels of interests, wherein each cluster-specific classifier is configured to generate labels of interest characterizing the same aspect of the transaction records;
• receiving a test transaction record;
• predicting a particular tag sequence cluster of the test transaction record using the cluster-level classifier;
• determining, from the plurality of cluster-specific classifiers, a particular cluster-specific classifier that corresponds to the particular tag sequence cluster;
• predicting a label of interest of the test transaction record using the determined particular cluster-specific classifier;
• and
• providing a representation of the test transaction record in association with the label of interest to a data consuming device for storage or data processing.

One or more non-transitory storage devices storing instructions that are operable, when executed by one or more computers, to cause the one or more computers to perform operations comprising:

• receiving labeled transaction records as training data, the transaction records including token sequences;
• forming, from a set of tags each representing a respective different aspect of the transaction records, tag-level sequence data from the token sequences, the tag-level sequence data including tag sequences corresponding to the token sequences, wherein for each tag sequence, each tag in the tag sequences being -is an respective abstraction of one or more respective tokens in the corresponding token sequence;
• clustering the tag-level sequence data into a plurality of tag sequence clusters based on distances between the tag sequences, wherein the distances between tag sequences are determined at least in part based on an ordering of the tags in the tag sequences;
• training a cluster-level classifier that predicts mappings between i) the transaction records and ii) the tag sequence clusters;
• training a plurality of cluster-specific classifiers, wherein:
• each cluster-specific classifier corresponds to a respective tag sequence cluster, and
• each cluster-specific classifier corresponding to a respective tag sequence cluster predicts mappings between i) transaction records that have been classified by the cluster-level classifier as belonging to the respective tag sequence cluster and ii) one or more labels of interests, wherein each cluster-specific classifier is configured to generate labels of interest characterizing the same aspect of the transaction records;
• receiving a test transaction record;
• predicting a particular tag sequence cluster of the test transaction record using the cluster-level classifier;
• determining, from the plurality of cluster-specific classifiers, a particular cluster-specific classifier that corresponds to the particular tag sequence cluster;
• predicting a label of interest of the test transaction record using the determined particular cluster-specific classifier;
• and
• providing a representation of the test transaction record in association with the label of interest to a data consuming device for storage or data processing.