Generating and applying a trained structured machine learning model for determining a semantic label for content of a transient segment of a communication

A computer-implemented method, comprising:

• grouping a corpus of electronic communications into a plurality of clusters based on metadata associated with each communication;
• identifying, from communications of a particular cluster, a set of segments;
• classifying a plurality of the segments of the set of segments as transient segments, wherein classifying a given segment of the segments as a transient segment is based on determining variability of content of the given segment across the particular cluster satisfies one or more criteria, and wherein the classifying is performed without human access to content of the communications;
• generating a template for the cluster, the template defining an order of the transient segments, wherein the order of the transient segments is based on the particular cluster;
• for each communication of a training set of the communications of the particular cluster:
• annotating each of one or more of the transient segments with at least one corresponding semantic label, wherein the annotating is performed without human access to the content of the communications, and wherein the semantic label annotated for at least one transient segment of the transient segments is a given semantic label,
• annotating the at least one transient segment with a first probability that the given semantic label is correct for the at least one transient segment for the communication, and
• annotating the at least one transient segment with a second probability that the given semantic label is incorrect for the at least one transient segment for the communication;
• generating training examples that each define a plurality of features for a corresponding one of the communications of the training set, the features including at least the annotated semantic labels for the transient segments, the order for the transient segments, and weights for each of the training examples determined based on a corresponding one of the first and second probabilities;
• and
• training a structured machine learning model for the template using the training examples, the trained structured machine learning model defining parameters for determining, for one or more of the transient segments, a corresponding probability that the given semantic label of the semantic labels is a correct label.

A computer-implemented method, comprising:

• grouping a corpus of electronic communications into a plurality of clusters based on metadata associated with each communication;
• identifying, from communications of a particular cluster, a set of segments;
• classifying a plurality of the segments of the set of segments as transient segments, wherein classifying a given segment of the segments as a transient segment is based on determining variability of content of the given segment across the particular cluster satisfies one or more criteria, wherein the classifying is performed without human access to content of the communications, and wherein the classifying comprises:
• determining a count of occurrences of the given segment across the particular cluster,
• determining the count of occurrences of the given segment across the particular cluster fails to satisfy a frequency threshold;
• and
• determining, based on the count of occurrences of the given segment across the particular cluster failing to satisfy the frequency threshold, the variability of the content of the given segment across the particular cluster satisfies one or more of the criteria;
• generating a template for the cluster, the template defining an order of the transient segments, wherein the order of the transient segments is based on the particular cluster;
• for each communication of a training set of the communications of the particular cluster, annotating each of one or more of the transient segments with at least one corresponding semantic label, the annotating performed without human access to the content of the communications;
• generating training examples that each define a plurality of features for a corresponding one of the communications of the training set, the features including at least the annotated semantic labels for the transient segments and the order for the transient segments;
• and
• training a structured machine learning model for the template using the training examples, the trained structured machine learning model defining parameters for determining, for one or more of the transient segments, a corresponding probability that a given semantic label of the semantic labels is a correct label.