Matt Marshall recently posted a story about a new search engine looking to raise a lot of money at a very high valuation, which has created quite a bit of buzz as people argue over whether or not the company has a chance, or deserves such a high valuation. Matt followed up with more details on the company, though he still expresses some reasonable skepticism. Like many people, my first reaction on hearing about it was that I can't remember a year that's gone by without someone claiming to have come out with a revolution in natural language search. However, when it comes to search engine news, no one can go through the history and explain why something is a bad idea quite like Danny Sullivan can. He lists out all the attempts at natural language search, and shows how each one failed (in some cases, miserably). He also points out that the problem with natural language search is that it requires everyone to change their behavior. As with any startup, when you're looking at their chances, the big question to ask is pretty simple: what problem does it solve? Plenty of people have figured out how to search with keywords. In fact, many of us find it more natural and faster than trying to construct a natural language query. So, while all the natural language search engines that come along insist that searches suck because they can't understand the the searcher, it's not clear that's the real problem. When people want to use a search engine, they want to find what they want. That means being able to search quickly. Dumping two or three keywords into a box is always going to be a lot faster than figuring out the natural language equivalent. So, perhaps someone can enlighten us. What is the problem natural language search solves?
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