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Lexxe in Forbes Article on Natural Language Search

Forbes.com has a special issue on language, including interesting articles and interviews by some of my favorite writers on Language.

I'm happy that natural language and semantic search was included in the special issue. Andy Greenberg from Forbes.com published his piece on language and search engines devoting a good portion of the article to Powerset and Hakia, featuring interviews with me and with Hakia's founder Riza Berkan. The article, entitled "Language Web-lish" starts off with Andy using Powerset's metaphor comparing people's current use of search engines to communicating like cavemen:

A question in English, like "What year was Hillary Clinton born?" becomes what he calls a primitive "keywordese": "Hillary Clinton born year."

"We have this great gift of human intelligence based around language," says Pell, "and now we have to translate it into a grunting pidgin language to interact with machines."

Andy described an example I showed him from Powerset:

When a user enters the question, "In what year was Hillary Clinton born?," Powerset's algorithm doesn't simply scour the Web for this collection of words in close proximity. Instead, it looks at pages with an eye for their meaning. Reading the sentence "Born to Dorothy and Hugh Rodham in 1947, Hillary Clinton is a New York senator," Powerset will disassemble the sentence's grammar and extract the fact of Hillary Clinton's birth date. That fact is then connected with the user's question, even if the word order of the result and the query didn't originally match.

Andy also went through an example from Hakia:

Taking the question "What drug is best for treating a urinary tract infection?" Riza Berkan points to the word "drug." Hakia's algorithm, he says, understands that the word contains a massive subset of concepts including synonyms and specific names of medicines. When it spots a term that falls into that subset, like "Amoxicillin," Hakia can substitute the medicine's name for the word "drug" in the result.

"You don't want the word 'drug,' you want the name of the drug," says Berkan. "That's a hidden failure in search engines, and people don't even know what they're missing."

Other natural language and semantic search companies mentioned included Cognition Search and Lexxe.

As is typical, my friend Peter Norvig at Google gets the last word in the article:

Google's Peter Norvig, the search giant's director of research, knows just how complex semantic algorithms can be: His Berkeley Ph.D. thesis tried to develop one in 1978. Every sentence of text, he says, took weeks to analyze. "The result was kind of like a dancing bear," he says. "It was amazing that it could dance at all, but we didn't expect it to star in the Moscow Ballet."

But that doesn't mean Google's engineers are idly watching semantic search from a distance, says Norvig. The company's thousands of engineers are looking at how to incorporate semantic analysis into a search algorithm. But semantic analysis is just one of many directions that Google's teams are exploring... "Basically, we just do whatever works," says Norvig. "Instead of trying to understand everything, we're trying to understand something about billions of pages a week."

But does that pragmatic approach leave Google vulnerable to an innovative start-up willing to risk its fate on building meaning-based search from scratch?

"It's unlikely," says Norvig. "But even car companies have to worry about anti-gravity machines."

I think that analogy is quite a stretch. It's more like big car companies having to worry about smaller companies focused on electric cars. They don't have to worry about this immediately but, at some point, this is going to be the future of their industry.

For the full article by Forbes, please go to http://www.forbes.com/business/2008/02/21/search-engine-semantic-tech-cx_ag_language_sp08_0221hakia.html.