Automated machine learning-based topic tagging system for educational content categorization

An online education platform for tagging and storing activities, comprising:

• a tagged activities database comprising a plurality of activities with each activity tagged with one or more topics;
• a topics taxonomy database that stores a taxonomy of topics;
• one or more servers configured to:
• remove low value words from a text,
• standardize the text into a desired format,
• combine words and/or phrases into a single string,
• access an embedding model to calculate a similarity score between an embedding for the single string and an embedding for each topic in the taxonomy,
• select a plurality of topics in the taxonomy database with the highest similarity score,
• using a machine learning model to retain classified topics determined to have a high level of relevance from the plurality of topics in the taxonomy database with the highest similarity score, and tag an activity with each of the retained classified topics classified as having a strong match;
• a tokenizer configured to tokenize the text into the words and/or phrases;
• a web interface configured to interface with web devices of a plurality of users and receive the activity and the text from one of the plurality of users;
• wherein
• the machine learning model comprises a processing unit, a memory and a neural network that comprises interconnected nodes organized in layers, configured for classifying each of the selected plurality of topics as having either a low, a middle or the high level of relevance, classifying each of the retained classified topics determined to have a high level of relevance as being either a bad, a weak or the strong match based at least in part on a text and relation to the activity;
• and
• the embedding model is configured for embedding the single string and embedding each topic in a taxonomy, wherein the embedding model comprises a neural network architecture or an advanced transformer-based model.

A method for tagging an educational activity with one or more topics, comprising:

• receiving a text that corresponds to the educational activity;
• removing low value words from the text;
• standardizing the text into a desired format;
• tokenizing the text into words and/or phrases;
• combining the tokenized words and phrases into a single string;
• generating an embedding for the single string using an embedding model, wherein the embedding model comprises a neural network architecture or an advanced transformer-based model;
• generating an embedding for each topic in a taxonomy of topics;
• calculating a cosine similarity score between the embedding for the single string and the embedding for each topic in the taxonomy;
• selecting a plurality of topics in the taxonomy that have the highest cosine similarity scores with the single string;
• classifying each topic in the selected plurality of topics using an artificial intelligence (AI) model as “Definitely Not Relevant”, “Not Sure”, and “Definitely Relevant”, wherein the AI model comprises a processing unit, a memory and a neural network that comprises interconnected nodes organized in layers;
• retaining the classified topics that are determined to be “Definitely Relevant”;
• classifying each of the retained topics using an artificial intelligence model as “Bad Match”, “Weak Match” and “Strong Match” based on at least part of the text and relation to the educational activity;
• and
• applying the retained topics classified as “Strong Match” as the one or more tags for the educational activity.