Abstract: The accuracy of existing machine learning models, software technologies, and computers are improved by using one or more machine learning models to map data inside structural elements, such as rows or columns, as found within a document to data objects of other documents, where the data objects are at least partially indicative of candidate categories that the data can belong to.

Abstract: The accuracy of existing machine learning models, software technologies, and computers are improved by using one or more machine learning models to predict a type of data that one or more numerical characters and/or one or more natural language word characters of a document correspond to. For instance, a Question Answering systems can be used to predict that a particular number value corresponds to a date, a billing amount, a page number, or the like.

Abstract: Particular embodiments receive a plurality of values associated with a document. One or more keywords associated with the document are also received. A first score is generated for each value, of the plurality of values. The generation of the first score excluding sending, over a computer network, a first request to one or more remote computing devices to generate the first score. Based at least in part on the generating of the first score, each value, of the plurality of values is ranked. The ranking of each value excluding sending, over the computer network, a second request to the one or more remote computing devices to rank each value. Based on the ranking, at least one value, of the plurality of values, is selected. The selecting being indicative that the at least one value is a candidate to be a constituent of the one or more keywords. Based on the ranking, an indicator indicating that the at least one value is the candidate to be the constituent of the one or more keywords is presented.

Abstract: The accuracy of existing machine learning models, software technologies, and computers are improved by using one or more machine learning models to map data inside structural elements, such as rows or columns, as found within a document to data objects of other documents, where the data objects are at least partially indicative of candidate categories that the data can belong to.

Abstract: The accuracy of existing machine learning models, software technologies, and computers are improved by estimating whether a particular page belongs to a same document as another page or whether the page belongs to a different document. Such document distinguishing can be based on deriving relationship information between a first feature vector representing the page and a second feature vector representing the other page. This also improves the user experience and model building experience, among other things.