Predictive Analytics by Eric Siegel
Author:Eric Siegel
Language: eng
Format: epub
Publisher: John Wiley & Sons
Published: 2013-01-28T05:00:00+00:00
Text Analytics
It was Greek to me.
—William Shakespeare
I’m completely operational, and all my circuits are functioning perfectly.
—HAL, the intelligent computer from 2001: A Space Odyssey (1968)
Science fiction almost always endows AI with the capacity to understand human tongues. Hollywood glamorizes a future in which we chat freely with the computer like a well-informed friend. In Star Trek IV: The Voyage Home (1986), our heroes travel back in time to a contemporary Earth and are confounded by its primitive technology. Our brilliant space engineer Scotty, attempting to make use of a Macintosh computer, is so accustomed to computers understanding the spoken word that he assumes its mouse must be a microphone. Patiently picking up the mouse as if it were a quaint artifact, he jovially beckons, “Hello, computer!”
2001: A Space Odyssey’s smart and talkative computer, HAL, bears a legendary, disputed connection in nomenclature to IBM (just take each letter back one position in the alphabet); however, author Arthur C. Clarke has strenuously denied that this was intentional. Ask IBM researchers whether their question answering Watson system is anything like HAL, which goes famously rogue in the film, and they’ll quickly reroute your comparison toward the obedient computers of Star Trek.
The field of research that develops technology to work with human language is natural language processing (NLP, aka computational linguistics). In commercial application, it’s known as text analytics. These fields develop analytical methods especially designed to operate across the written word.
If data is all Earth’s water, textual data is the part known as “the ocean.” Often said to compose 80 percent of all data, it’s everything we the human race know that we’ve bothered to write down. It’s potent stuff—content-rich because it was generated with the intent to convey not just facts and figures, but human knowledge.
But text, data’s biggest opportunity, presents the greatest challenge.
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