US 2018/0357569 A1Published application

Multi-modal declarative classification based on uhrs, click signals and interpreted data in semantic conversational understanding

AssigneeElement Data, Inc.

InventorsViswanath Vadlamani, Phani Vaddadi, Charles F. L. Davis

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Abstract

Examples are presented for a classification system that utilizes multiple classification models to adapt to any desired set of raw data to be classified. The classification system may include multiple classification models stored in a model repository. A truth set of the raw data may be used to evaluate the fitness of each of the stored classification models. The models may be scored and ranked to determine which is the most appropriate to use for real time classification of the raw data. The optimal classification model may be used in a classification engine to classify the raw data in real time. This generates a classified output that may be interacted with by a user. A user interface may be used to permit feedback of the classified output to be generated. This feedback may then be transmitted to the offline system and recorded to further improve the classification models.

Claims20

Dependent claims

A classification system for classifying documents in real time using natural language processing, the classification system comprising:

at least one processor;
at least one memory communicatively coupled to the processor, the at least one memory storing classification modules comprising:
a tenant and domain judgement factory configured to classify a subset of documents from a present set of documents to be classified, and generate a golden set of documents that represents an accurate classification of the subset of documents;
a model repository configured to store a plurality of classification models, wherein each classification model was generated to originally classify a different set of documents than the present set of documents to be classified;
a metrics and evaluation system configured to evaluate a fitness level of each of the plurality of classification models to the present set of documents to be classified, by classifying the golden set using said each classification model and determining which classification model generates the most accurate classification of the golden set;
and
a classification engine configured to perform, in real time, classification on the remaining present set of documents to be classified, using the classification model that generated the most accurate classification of the golden set.

4 more elements

A non-transitory computer readable medium comprising instructions that, when executed by a processor of a classification system, cause the processor to perform operations comprising:

receiving classifications for a subset of documents from a present set of documents to be classified,
generating a golden set of documents using the received classifications that represents an accurate classification of the subset of documents;
accessing a plurality of classification models from a model repository, wherein each classification model was generated to originally classify a different set of documents than the present set of documents to be classified;
evaluating a fitness level of each of the plurality of classification models to the present set of documents to be classified, by performing classification on the golden set using said each classification model and determining which classification model generates the most accurate classification of the golden set;
and
performing, in real time, classification on the remaining present set of documents to be classified, using the classification model that generated the most accurate classification of the golden set.

3 more elements

Dependencies are inferred from the claim text.