Idea Navigation

Last summer, my colleague Vladimir Zelevinsky worked with two interns, Robin Stewart (MIT) and Greg Scott (Tufts), on a novel approach to information exploration. They call it "idea navigation": the basic idea is to extract subject-verb-object triples from unstructured text, group them into hierarchies, and then expose them in a faceted search and browsing interface. I like to think of it as an exploratory search take on question answering.

We found out later that Powerset developed similar functionality that they called "Powermouse" in their private beta and now call "Factz". While the idea navigation prototype is on a smaller scale (about 100k news articles from October 2000), it does some cool things that I haven't seen on Powerset, like leveraging verb hypernyms from WordNet.

Click on the frame below to see the presentation they delivered at CHI '08.



Idea Navigation: Structured Browsing for Unstructured Text

A Lofty Goal

The blogosphere is all atwitter with Powerset's public launch last night. Over at Techcrunch, Michael Arrington refers to their approach as a lofty goal.

But I'd like us to dream bigger. In the science fiction stories that inspired me to study computer and information science, the human-computer interface is not just natural language input. It's dialogue. The authors do not treat machine understanding of unambiguous requests as a wonder, but instead take it for granted as an artifact of technical progress. Indeed, the human-computer interface only becomes relevant to the plot when communication breaks down (aka "that does not compute").

Ever since I hacked a BASIC version of ELIZA on a Commodore 64, I've felt the visceral appeal of natural language input as an interface. Conversely, the progress of speech synthesis attests to our desire to humanize the machine's output. It is as if we want to reduce the Turing Test to a look-and-feel.

But the essence of dialogue lies beneath the surface. The conversations we have with machines are driven by our information needs, and should be optimized to that end. Even we human drop natural language among ourselves when circumstances call for more efficient communication. Consider an example as mundane as Starbucks baristas eliciting and delegating a latte order.

In short, let's remember that we want to talk with our computers, not just at them. Today's natural language input may be a step towards that end, or it may be just a detour.

Powerset: Public Launch Later Today

As a member of the Powerset private beta, I just received this announcement:

Greetings Powerlabbers,

Later today, Powerset is going to launch the first publicly available version of our product. Since you've been active in the Powerlabs community, we wanted to give you a special heads-up to look for our release. Your suggestions, help, feedback, bug reports, and conversation have helped us immensely in creating an innovative and useful product. We hope that you'll continue to be active in Powerlabs and make more great suggestions.

More information will be posted on Powerset's blog later today, so keep your eye out for updates. Also, consider following us on Twitter or becoming a fan of Powerset on Facebook.

If you have a blog, we'd especially appreciate it if you'd write a blog post about your experience with this first Powerset product. Since you've been on the journey with us, your insight will be helpful in showing other people all of the amazing features in this release.

Again, we want to extend special thanks to you for sticking with us. We hope you feel almost as invested in this release as we are.

Thanks!

The Powerset Team

As loyal readers know, I've posted my impressions in the past. Now that the beta will be publicly available, I'm curious to hear impressions from you all.

The Search for Meaning

By a fortuitous coincidence, I had the opportunity to see two consecutive presentations from search engine companies banking on natural language processing (NLP) to power the next generation of search. The first was from Ron Kaplan, Chief Technology and Science Officer of Powerset, who presented at Columbia University. The second was from Christian Hempelmann, Chief Scientific Officer of hakia, who presented at New York Semantic Web Meetup.

The Powerset talk was entitled "Deep natural language processing for web-scale indexing and retrieval." Jon Elsas, who attended the same talk earlier this week at CMU, did an excellent job summarizing it on his blog. I'll simply express my reaction: I don't get it. I have no reason to doubt that their NLP pipeline is best-in-class. The team has impressive credentials. But I see no evidence that they have produced better results than keyword search. After participating in their private beta for several months, I'd hoped that the presentation would help me see what I'd missed. I specifically asked Ron what measures they used to evaluate their system, and he was mum. So now I am more unconvinced that ever, though, to steal a line from a colleague, I cannot reconcile their enthusiasm with their results.

The hakia talk was entitled "Search for Meaning." Christian started by making the case for a semantic, rather than statistical approach to NLP. He then presented hakia's technology in a fair amount of detail, including walking through examples of worse sense disambiguation using context. I'm not convinced that semantics trump statistics, but I thoroughly enjoyed the presentation, and was intrigued enough to want to learn more. I find the company refreshingly open about its technology (not to mention that their beta is public), and I hope it works well enough to be practical.

Still, I'm not convinced the NLP is either the right answer or the right question. I'm no expert on the history of language, but it's clear that natural languages are hardly optimal means of communication, even among human beings. Rather, they are artifacts of our satisficing and resisting change. Since we are lucky enough to not have developed expectations that people can communicate with computers using natural language (HAL and Star Trek notwithstanding), why take a step backwards now? Rather than advocating for inefficient, unreliable communication mechanisms like natural language, we should be figuring out ways to make communication more efficient.

To use an analogy, there's a reason that programming languages have strict rules, and that compilers output errors rather than just trying to guess what you mean. The mild inconvenience upstream is a small cost, compared to the downstream benefits of unambiguous communication. I'm not suggesting that people start speaking in formal languages. But I do feel we should strive for a dialog-oriented approach where both the human and the computer have confidence in their mutual understanding. I can't resist a plug for HCIR.