Machine learning based extraction of partition objects from electronic documents

An apparatus, comprising:

• a processor;
• and
• a memory storing instructions, executable by the processor, to:
• generate a dataset including a plurality of value pairs, each value pair from the plurality of value pairs including an error-free value and an error-containing value, at least one error-free value included in at least one value pair from the plurality of value pairs modified to generate at least one error-containing value included in the at least one value pair from the plurality of value pairs;
• train a machine learning model with the dataset to produce a trained machine learning model, training the machine learning model including
• applying a semantic model included in the machine learning model and not a similarity model included in the machine learning model,
• applying, after applying the semantic model and not the similarity model, the similarity model and the semantic model, and
• in response to applying the semantic model and the similarity model, applying the similarity model and a multi-layer perceptron model included in the machine learning model without applying the semantic model;
• detect, via the trained machine learning model, an error in an electronically-stored file;
• in response to detecting the error, convert the electronically-stored file into a modified electronically-stored file that does not include the error;
• identify a set of objects associated with the modified electronically-stored file;
• and
• in response to a user interaction with a representation of an object from the set of objects, update the trained machine learning model based on the user interaction, to have more weight for at least one of retrieval or ranking, and to have less weight for semantic searching.

A non-transitory, processor-readable medium storing instructions to cause a processor to:

• generate a plurality of value pairs, each value pair from the plurality of value pairs including an error-free value and an error-containing value, at least one error-free value included in at least one value pair from the plurality of value pairs modified to generate at least one error-containing value included in the at least one value pair from the plurality of value pairs;
• train a machine learning model with the plurality of value pairs to produce a trained machine learning model, training the machine learning model including
• applying a semantic model included in the machine learning model and not a similarity model included in the machine learning model,
• applying, after applying the semantic model and not the similarity model, the similarity model and the semantic model, and
• in response to applying the semantic model and the similarity model, applying the similarity model and a multi-layer perceptron model included in the machine learning model without applying the semantic model;
• detect, via the trained machine learning model, an error in an electronically-stored file;
• in response to detecting the error, convert the electronically-stored file, via the trained machine learning model, into a modified electronically-stored file that does not include the error;
• identify a set of objects associated with the modified electronically-stored file;
• and
• in response to a user interaction with a representation of an object from the set of objects, update the trained machine learning model based on the user interaction, to have more weight for at least one of retrieval or ranking, and to have less weight for semantic searching.