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General electric scisor analyzes financial news

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Reference no: EM132803046

Read the case study given below and answer the questions at the end of the case.

General Electric's SCISOR analyzes financial news

General Electric's Research and Development Center has developed a natural language system called SCISOR (System for Conceptual Information Summarization, Organization, and Retrieval) that performs text analysis and question-answering in a limited, predefined subject area (called a constrained domain). One application of this system deals with analyzing financial news. For example, SCISOR automatically selects and analyzes stories about corporate mergers and acquisitions from the online financial service of Dow Jones. Ii is able to process news in less than 10 seconds per story. First, it determines whether the story is about a corporate merger or acquisition. Then, it selects information such as the target, suitor, and price per share. The system allows the user to browse and ask questions such as, "What price was offered for Polaroid?" or "How much was Bruck Plastics sold for?"

The system's effectiveness was demonstrated in testing, when it proved to be 100 percent accurate in identifying all 31 mergers and acquisitions stories that were included in a universe of 731 financial news releases from the newswire service.

A similar application is a Web-based personalized news system that was developed in Singapore to track business news available in English, Chinese, and Malay, summarize it, and extract desired personalized news in any of these languages.

Questions:

(a) What are the benefits of analyzing financial news via a machine?

(b) What other applications might be developed with this type of system?

(c) How could such a system be combined with an Internet news dissemination portal such as money.cnn.com?

(d) Discuss the reliability factor of such a system.

Reference no: EM132803046

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