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We present an approach and a system for collective disambiguation of entity mentions occurring in natural language text. Given an input text, the system spots mentions and their candidate entities. Candidate entities across all mentions are jointly modeled as binary nodes in a Markov Random Field. Their edges correspond to the joint signal between pairs of entities. This facilitates collective disambiguation of the mentions achieved by performing MAP inference on the MRF in a binary label space. Our model also allows for a natural treatment of mentions that either have no entity attached or have more than one attachments. By restricting cliques to nodes and edges and with a submodularity assumption on their potentials, we get an inference problem that is efficiently solved using graph min cut.

The Indian economy was in a difficult situation at the time of British rule. India was doing the development wants not of herself, but foreign land. The state that should have been responsible for a breakthrough in agriculture and industry, denied playing even a minor role in this case. On the other side, during the half-century before India's freedom, the world was seeing rapid development and growth in agriculture, trade and industry -on the behest of an active role being played by the states. U.K. rule never made any changes for the development of India (occupational sector, industry, social sector). During freedom, India's literacy was just 17 percent, with a life expectancy of only 32.5 years. Therefore, once India got independence, systematic organization of the economy was a real challenge for the government of that time. The need for growth and development was in huge demand in front of the Indian leadership -as the country was riding on the promises and people ambitions. At present India is ranked the seventh-largest economy, and third largest in terms of Purchasing Power Parity. The Gross Domestic