Machine-learning natural language processing classifier for content classification

A non-transitory computer-readable medium that stores instructions to classify content based on a machine-learning natural language processing (ML-NLP) classifier, the instructions, when executed by a processor, program the processor to:

• access a general corpus of data comprising text;
• train the ML-NLP classifier based on a base model having a plurality of layers that is pre-trained on the general corpus of data;
• access a domain-specific corpus of data comprising text relating to a specific domain;
• further train the ML-NLP classifier based on the domain-specific corpus of data;
• access a topic-specific corpus of data labeled according to a plurality of topics of interest, wherein at least one output layer for performing a classification task that predicts whether the content relates to one of the plurality of topics of interest is appended to the base model, wherein the general corpus of data, the domain-specific corpus of data, and the topic-specific corpus of data are independent of one another;
• further train the ML-NLP classifier having the base model and the appended at last one output layer based on the topic-specific corpus of data;
• and
• provide a classification service based on the trained ML-NLP classifier, wherein the classification service generates a respective classification of whether or not content relates to one or more of a plurality of topics of interest associated with a domain.

A method of classifying content based on a machine-learning natural language processing (ML-NLP) classifier, the method comprising:

• receiving, by a processor, content;
• determining, by the processor, that the content relates to an entity of interest;
• classifying, by the processor, the content based on the ML-NLP classifier, the ML-NLP classifier being based on a base model having a plurality of layers that is pre-trained on a general corpus of data, further pre-trained a domain-specific corpus of data, wherein at least one output layer for performing a classification task that predicts whether the content relates to one of a plurality of topics of interest is appended to the base model, wherein the plurality of topics of interested is provided by a subscribing user and wherein base model with the at least one appended output layer is further trained on a topic-specific corpus of data labeled according to a plurality of topics of interest, wherein the general corpus of data, the domain-specific corpus of data, and the topic-specific corpus of data are independent of one another;
• determining, by the processor, based on the classified content, that the content relates to one or more of the plurality of topics;
• and
• causing, by the processor via a graphical user interface of the subscribing user that is subscribed to receive content relating to the entity of interest and the plurality of topics, the classifications to be transmitted to the subscribing user.