Automatic classification of phone calls using representation learning based on the hierarchical pitman-yor process
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United States of America
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Feb 18, 2025
Grant Date -
Feb 23, 2023
app pub date -
Aug 9, 2021
filing date -
Aug 9, 2021
priority date (Note) -
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status (Latency Note)
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Abstract
Embodiments of the disclosed technology include a representation learning model for classification of natural language text. In embodiments, a classification model comprises a feature model and a classifier. The feature model may be hierarchical in nature: data may pass through a series of representations, decreasing in specificity and increasing in generality. Intermediate levels of representation may then be used as automatically learned features to train a statistical classifier. Specifically, the feature model may be based on a hierarchical Pitman-Yor process. In embodiments, once the feature model has been expressed as a Bayesian Belief Network and some aspect of the feature model has been selected for prediction, the feature model may be attached to the classifier. In embodiments, after training, potentially using a mix of labeled and unlabeled data, the classification model can be used to classify documents such as call transcripts based on topics of conversation represented in the transcripts.
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International Classification(s)
- 2021 Application Filing Year
- G06N Class
- 16935 Applications Filed
- 6768 Patents Issued To-Date
- 39.97 % Issued To-Date
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- G06N Class
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