1. A method of ranking candidate answers to a natural language question, the method comprising the steps of: a computer identifying first contextual information about a user of a mobile device, the first contextual information including (1) interests of the user based on historical usage of the mobile device by the user to run applications on the mobile device, visit websites via a browser provided by the mobile device, display textual articles on the mobile devices, enter terms into a search engine via the mobile device, and capture multimedia content, (2) a geographic location of the user provided by a global positioning system (GPS) coupled to the mobile device, (3) data specifying characteristics of an environment in proximity to the user, the characteristics of the environment being detected and measured by a first sensor coupled to the mobile device, and (4) data specifying a bodily function of the user, the bodily function being detected and measured by a second sensor coupled to the mobile device; based on the first contextual information, the computer determining a prioritization of definitions of terms; based on the first contextual information and the prioritization of the definitions of the terms, the computer generating a lexicon of the terms; using a forecaster that employs mobile-based time series manipulation and pattern recognition and based on (1) the historical usage of the mobile device by the user to run the applications on the mobile device, visit the websites via the browser provided by the mobile device, display the textual articles on the mobile devices, enter the terms into the search engine via the mobile device, and capture the multimedia content, (2) the geographic location of the user provided by the GPS coupled to the mobile device, (3) the data specifying characteristics of the environment in proximity to the user, and (4) the data specifying the bodily function of the user, the computer forecasting second contextual information that indicates future behavior of the user; based on the forecasted second contextual information, the computer performing word sense disambiguation of the terms in the lexicon and adjusting the prioritization of the definitions of the terms in the lexicon; based on the word sense disambiguation of the terms in the lexicon and the adjusted prioritization of the definitions of the terms, the computer modifying the candidate answers to the natural language question; and based in part on the adjusted prioritization of the definitions of the terms in the lexicon, the modified candidate answers, and the forecasted second contextual information, the computer ranking the modified candidate answers to the natural language question, the highest ranked modified candidate answer being more likely to be a correct answer to the natural language question than the other candidate answers.