Lexalytics Digests Wikipedia, Sees Text Analysis Markets Broaden to Include Search, Travel, Law

technologies has started to open up. Which also opens the door for plenty of competition, of course.

For its part, Lexalytics is doing more in the field of Web search these days, Catlin says, working with companies like Endeca, the Cambridge, MA-based e-commerce search firm. Other emerging markets for Lexalytics’ software include electronic discovery and digital forensics for the legal industry, and electronic medical records in healthcare. “We’re broadening out across a lot of things [besides sentiment analysis],” Catlin says. “The new leads are extremely horizontal.”

That’s because “the way people speak and text is a generic problem,” Catlin says. And his company’s technology is fundamentally about understanding “who’s talking, to whom, and what are they talking about?”

But one longstanding problem with semantic analysis is that it’s hard to apply the same technology across different sectors (travel and law, say), where similar words can have widely different meanings and connotations. Perhaps Lexalytics’ effort with Wikipedia—and IBM’s much larger project with Watson, the Jeopardy-playing machine—are examples of a new, brute-force approach to solving the problem using more computational horsepower. (It worked for chess, after all.)

The question for Lexalytics is how long it can keep its competitive advantage over big players like IBM, Google, and Microsoft—in terms of what its technology does best—as well as other hungry startups vying for a piece of the text-analysis pie.

“We’re geeky engineer types. You can’t ‘protect’ your [intellectual property] per se, so you do really good work and you try to build a mousetrap and stay ahead of the game,” Catlin says. “The truth is, what IBM built with Watson took them a long, long time. We’re going to put out something that will enable people to build a good chunk of what they did. But it’s not easy stuff.”

Indeed, Catlin is talking about solving a very deep problem—and one that could pay off in unexpected ways down the road. “If you can take verbal questions or written words and understand what they’re asking,” he says, “then the applications are as wide as your brain.”

Author: Gregory T. Huang

Greg is a veteran journalist who has covered a wide range of science, technology, and business. As former editor in chief, he overaw daily news, features, and events across Xconomy's national network. Before joining Xconomy, he was a features editor at New Scientist magazine, where he edited and wrote articles on physics, technology, and neuroscience. Previously he was senior writer at Technology Review, where he reported on emerging technologies, R&D, and advances in computing, robotics, and applied physics. His writing has also appeared in Wired, Nature, and The Atlantic Monthly’s website. He was named a New York Times professional fellow in 2003. Greg is the co-author of Guanxi (Simon & Schuster, 2006), about Microsoft in China and the global competition for talent and technology. Before becoming a journalist, he did research at MIT’s Artificial Intelligence Lab. He has published 20 papers in scientific journals and conferences and spoken on innovation at Adobe, Amazon, eBay, Google, HP, Microsoft, Yahoo, and other organizations. He has a Master’s and Ph.D. in electrical engineering and computer science from MIT, and a B.S. in electrical engineering from the University of Illinois, Urbana-Champaign.