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 way that reflects (and also has helped to shape) the innate ability provided by the brain's neural networks. In computer science and the information technology that it enables, it has been a long-term challenge to develop the ability in computers to do natural language processing and machine learning. A rich variety of techniques have been researched, from dictionary-based methods that use the knowledge encoded in lexical resources, to supervised machine learning methods in which a classifier is trained for each distinct word on a corpus of manually sense-annotated examples, to completely unsupervised methods that cluster occurrences of words, thereby inducing