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 space. However, these supervised methods are subject to a new knowledge acquisition bottleneck since they rely on substantial amounts of manually sense-tagged corpora for training, which are laborious and expensive to create. Because of the lack of training data, many word sense disambiguation algorithms use semi-supervised learning, which allows both labeled and unlabeled data. The Yarowsky algorithm was an early example of such an algorithm. It uses the ‘One sense per collocation’ and the ‘One sense per discourse’ properties of human languages for word sense disambiguation. From observation, words tend to exhibit only one sense in most given discourse and