Written-modality prosody subsystem in a natural language understanding (NLU) framework

A method of operating a prosody subsystem of a natural language understanding (NLU) framework, comprising:

• dividing a conversation log comprising plurality of messages into a plurality of conversation channel groups based on a first set of prosodic cues;
• dividing each of the plurality of conversation channel groups into a plurality of sessions based on a second set of prosodic cues;
• dividing each of the plurality of sessions into a plurality of conversation segments based on a third set of prosodic cues;
• dividing each of the plurality of conversation segments into a plurality of utterances based on a fourth set of prosodic cues;
• dividing each of the plurality of utterances into a plurality of intent segments based on a fifth set of prosodic cues, wherein the second, third, fourth, and fifth sets of prosodic cues comprise temporal prosodic cues, written prosodic cues, or a combination thereof;
• and
• providing the plurality of intent segments, the plurality of utterances, the plurality of conversation segments, or the plurality of sessions, or a combination thereof, as inputs to processes of the NLU framework.

A non-transitory, computer-readable medium storing instructions of a natural language understanding (NLU) framework executable by one or more processors of a computing system, the instructions comprising instructions to:

• process, via a prosody subsystem of the NLU framework, a conversation log based on prosodic cues to divide the conversation log into conversation channel groups, to divide the conversation channel groups into sessions, to divide the sessions into conversation segments, to divide the conversation segments into utterances, and to divide the utterances into intent segments;
• provide the intent segments as inputs to a first training process for a machine-learning (ML)-based parser of the NLU framework, wherein, within the first training process, the NLU framework is configured to apply a plurality of other parsers of the NLU framework to generate a plurality of utterance trees for each intent segment, and in response to determining that a majority of the plurality of utterance trees for a particular intent segment are the same utterance tree, update a model of the ML-based parser such that the ML-based parser generates the same utterance tree for the particular intent segment;
• provide the utterances as inputs to a second training process for a vocabulary subsystem of the NLU framework, wherein, within the second training process, the utterances are used to generate a first plurality of word vectors for a refined word vector distribution model that replaces a word vector distribution model of the vocabulary subsystem, wherein the NLU framework is configured to use the refined word vector distribution model to determine a suitable word vector for words of received natural language requests;
• provide the intent segments as inputs to a semantic mining pipeline of the NLU framework, wherein the semantic mining pipeline is configured to:
• generate intent vectors for the intent segments;
• generate meaning clusters of intent vectors based on distances between the intent vectors;
• detect stable ranges of cluster radius values for the meaning clusters;
• and
• generate an intent/entity model from the meaning clusters and the stable ranges of cluster radius values, wherein the intent/entity model stores relationships between a representative intent of each of the meaning clusters and corresponding intent segments as sample utterances, and wherein the NLU framework is configured to use the intent/entity model to classify intents in the received natural language requests;
• and
• provide the sessions, the conversation segments, or a combination thereof, as inputs to a persona of a reasoning agent/behavior engine (RA/BE) of the NLU framework, wherein RA/BE is configured to generate an episode frame tree set in a persona context database of the persona based on each of the sessions, the conversation segments, or the combination thereof, wherein the episode frame tree set comprises an episode start time and an episode end time that are heuristically determined from the sessions, the conversational segments, or the combination thereof.