Machine Learning Systems And Methods For Interactive Concept Searching Using Attention Scoring

A machine learning method for interactive concept search, comprising:

• receiving textual data;
• identifying one or more word representations of the textual data;
• receiving a concept;
• determining a score indicative of a likelihood of each of the one or more word representations being representative of the concept using an attention scoring process having a temperature variable;
• generating a dataset for training and evaluation based at least in part on the score, the dataset comprising the one or more word representations and the concept;
• and
• processing the dataset to train one or more deep active learning models capable of the interactive concept search.

A non-transitory computer readable medium having instructions stored thereon for interactive concept search which, when executed by a processor, causes the processor to carry out the steps of:

• receiving textual data;
• identifying one or more word representations of the textual data;
• receiving a concept;
• determining a score indicative of a likelihood of each of the one or more word representations being representative of the concept using an attention scoring process having a temperature variable;
• generating a dataset for training and evaluation based at least in part on the score, the dataset comprising the one or more word representations and the concept;
• and
• processing the dataset to train one or more deep active learning models capable of the interactive concept search.