Machine learning model dynamic content classification and contextualization

A system for providing a dynamic content classification and contextualization system using machine learning models, comprising:

• one or more processors;
• and
• a memory comprising instructions that, when executed by the one or more processors, cause the system to:
• determine, using a machine learning model, a classification of content contained in an input provided to the machine learning model, wherein the machine learning model is trained to identify content types and distinguish the content types with an associated level of confidence using a supervised learning process by iteratively adjusting tunable parameters of the machine learning model based on training data associated with stored content classifications;
• generate, using the machine learning model, a confidence score for the classification;
• extract one or more entities from the content based on determining that the confidence score is below a threshold score, wherein the extracting comprises parsing the input, generating tokenized representations, and assigning entity labels based on detecting learned features in the tokenized representations;
• retrieve, using a language processing machine learning model, contextual data from a searchable knowledge base based on the one or more entities extracted from the content;
• generate, using the machine learning model, an updated classification based on the retrieving;
• and
• perform an action based on the updated classification.

A non-transitory computer readable medium comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to:

• determine, using a machine learning model, a classification of content contained in an input provided to the machine learning model, wherein the machine learning model is trained to identify content types and distinguish the content types with an associated level of confidence using a supervised learning process by iteratively adjusting tunable parameters of the machine learning model based on training data associated with stored content classifications;
• generate, using the machine learning model, a confidence score for the classification;
• extract one or more entities from the content based on determining that the confidence score is below a threshold score, wherein the extracting comprises parsing the input, generating tokenized representations, and assigning entity labels based on detecting learned features in the tokenized representations;
• retrieve, using a language processing machine learning model, contextual data from a searchable knowledge base based on the one or more entities extracted from the content;
• generate, using the machine learning model, an updated classification based on the retrieving;
• and
• perform an action based on the updated classification.