Heuristic and non-semantic prediction of the cost to find and review data relevant to a task

A computer program product, the computer program product comprising a computer readable storage medium having program code embodied therewith, the program code executable by at least one processor to perform:

• accessing a corpus of documents for a domain;
• accessing terms associated with the domain, wherein the terms have an order on a list, with terms having more relative importance to the domain being higher on the list;
• for each of the documents,
• determining term positional dispersion for each of the terms in the ordered list associated with the domain using a number of term occurrences in the document for a given term, a positional mean of relative position of the given term in the document, and a positional value of the given term at a particular position;
• and
• determining a document review quanta for the document based on a summation of the term positional dispersion for each term in that document adjusted by a weight;
• and
• selecting a subset of documents in the corpus of documents that are to be reviewed based on the document review quanta for each of the selected documents exceeding a threshold.

A computer system, comprising:

• one or more processors, one or more computer-readable memories and one or more computer-readable, tangible storage devices;
• and
• program instructions, stored on at least one of the one or more computer-readable, tangible storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to perform operations comprising:
• accessing a corpus of documents for a domain;
• accessing terms associated with the domain, wherein the terms have an order on a list, with terms having more relative importance to the domain being higher on the list;
• for each of the documents,
• determining term positional dispersion for each of the terms in the ordered list associated with the domain using a number of term occurrences in the document for a given term, a positional mean of relative position of the given term in the document, and a positional value of the given term at a particular position;
• and
• determining a document review quanta for the document based on a summation of the term positional dispersion for each term in that document adjusted by a weight;
• and
• selecting a subset of documents in the corpus of documents that are to be reviewed based on the document review quanta for each of the selected documents exceeding a threshold.