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 whole corpus is consumed, or until a given maximum number of iterations is reached. Other semi-supervised techniques use large quantities of untagged corpora to provide co-occurrence information that supplements the tagged corpora. These techniques have the potential to help in the adaptation of supervised models to different domains. Also, an ambiguous word in one language is often translated into different words in a second language depending on the sense of the word. Word-aligned bilingual corpora have been used to infer cross-lingual sense distinctions, a kind of semi-supervised system. Unsupervised learning is the greatest challenge for WSD researchers. The underlying assumption