21. A method for entity disambiguation for implementation by one or more data processors, the method comprising: using, by at least one of the data processors, cross-document disambiguation to identify an entity across a plurality of context domains, each domain comprising a finite set of context items, the context items comprising standardized names derived in an entity information extraction phase of within-document disambiguation; using, by at least one of the data processors, a logarithm of an inverse name frequency which comprises a number of standard names with which a context item appears as a weight indicating salience of each context item, wherein co-occurrence with a common name provides less indication that two mentions correspond to a same entity than co-occurrence with an uncommon name; using, by at least one of the data processors, a sparse count vector for recording all of the items that co-occur with a particular entity; creating, by at least one of the data processors, a sparse count vector of title tokens that occur with an entity; and computing, by at least one of the data processors, inverse name frequency weights for said title tokens.
24. The method of claim 21 , further comprising: defining, by at least one of the data processors, a separate distance measure per each specific context domain to discount co-occurrence with multiple items, as well as quantify an unexpected lack of shred co-occurrence; wherein a score produced by each distance measure is loosely interpreted as a function of the likelihood of two randomly generated contexts sharing an observed degree of similarity.