Multiple instance learning for content feedback localization without annotation

An apparatus comprising:

• one or more processors;
• and
• memory storing instructions that, when executed by the one or more processors, cause the one or more processors to:
• receive a plurality of textual data items;
• for each textual data item of the plurality of textual data items:
• identify a corresponding item-level score;
• and
• associate the item-level score with each sentence of one or more sentences of the textual data item;
• create a dataset comprising a plurality of sentences based on the plurality of textual data items;
• convert each sentence of the plurality of sentences into a corresponding vector of a plurality of vectors;
• based on processing the plurality of vectors using a k-nearest neighbors model, determine a number of sentences whose corresponding vectors are nearest to a vector corresponding to a first sentence;
• based on averaging the item-level scores associated with the number of sentences, calculate a sentence-level score for the first sentence;
• and
• based on the sentence-level score, localize the first sentence within a context.

A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to:

• receive a plurality of textual data items;
• for each textual data item of the plurality of textual data items:
• identify a corresponding item-level score;
• and
• associate the item-level score with each sentence of one or more sentences of the textual data item;
• create a dataset comprising a plurality of sentences based on the plurality of textual data items;
• convert each sentence of the plurality of sentences into a corresponding vector of a plurality of vectors;
• based on processing the plurality of vectors using a k-nearest neighbors model, determine a number of sentences whose corresponding vectors are nearest to a vector corresponding to a first sentence;
• based on averaging the item-level scores associated with the number of sentences, calculate a sentence-level score for the first sentence;
• and
• based on the sentence-level score, localize the first sentence within a context.