Fiscal gap Fiscal gap The fiscal gap is a measure of a government's total indebtedness proposed by economists Laurence Kotlikoff and Alan Auerbach, who define it as the difference between the present value of all of government's projected financial obligations, including future expenditures, including servicing outstanding official federal debt, and the present value of all projected future tax and other receipts, including income accruing from the government's current ownership of financial assets. According to Kotlikoff and Auerbach, the "fiscal gap" accounting method can be used to calculate the percentage of necessary tax increases or spending reductions needed to close the fiscal gap
Word-sense disambiguation one typically cooks food, one can disambiguate the word bass in "I am cooking basses" (i.e., it's not a musical instrument). Supervised methods are based on the assumption that the context can provide enough evidence on its own to disambiguate words (hence, common sense and reasoning are deemed unnecessary). Probably every machine learning algorithm going has been applied to WSD, including associated techniques such as feature selection, parameter optimization, and ensemble learning. Support Vector Machines and memory-based learning have been shown to be the most successful approaches, to date, probably because they can cope with the high-dimensionality of the feature