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Post-Googleism At IBM With Piquant
kamesh writes "James Fallows of the New York Times reports an interesting search technology that IBM is developing. IBM demonstrated a system called Piquant, which analyzed the semantic structure of a passage and therefore exposed 'knowledge' that wasn't explicitly there. After scanning a news article about Canadian politics, the system responded correctly to the question, 'Who is Canada's prime minister?' even though those exact words didn't appear in the article. What do you think?"
They don't come out and say it, but it sounds like it's just a big ol' LSI System. It works really well for some types of searching, but I'm not sure if such a thing would out perform google for a general purpose search engine.
"Latent semantic indexing adds an important step to the document indexing process. In addition to recording which keywords a document contains, the method examines the document collection as a whole, to see which other documents contain some of those same words. LSI considers documents that have many words in common to be semantically close, and ones with few words in common to be semantically distant. This simple method correlates surprisingly well with how a human being, looking at content, might classify a document collection. Although the LSI algorithm doesn't understand anything about what the words mean, the patterns it notices can make it seem astonishingly intelligent."
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Disclaimer: I haven't read the article; however, I was somewhat involved in research in this field in late 2003 and early 2004.
What the summary of the article claims IBM is developing-- a technology for getting the semantics behind an arbitrary sentence on the web-- is the Holy Grail of the discipline of Natural Language Processing (NLP) and very, very, very, _very_ far away at this point. Many people believe that we cannot ever get there (that's the point of a Holy Grail, after all), but I don't want to be quite as pessimistic (or realistic?) at this point.
The problem here is that English (or any other natural language, for that matter) isn't SML, or Haskell, or some other language with a well-defined denotational semantics. Natural language suffers from at least three problems that make it very tough to gather anything useful from a given piece of text:
(1) Grammar. Natural language isn't typechecked, and frequently uses incomplete sentences, which makes it hard to develop grammars (context-free, context-free probabilistic, lambek-style/proofnet-style or whatever else people have come up with) for it.
(2) Anaphora resolution. "I saw a dog on the street this morning. It was barking". So who's barking, street or dog? Gramatically, both would be possible; only with prior knowledge we can see that we're talking about the dog here.
(3) Polysemy. What does "play" mean, taken by itself? It can be used for different meanings in "to play a game", "a play of words", "a terrific shakespearian play" etc.; you might want to have a look at wordnet one of these days to get a feeling for this. Not knowing which meaning an arbitrary occurence of "play" refers to means that you have to try lots of options when parsing, LSIing or whatever else you do (though most people simply ignore this problem in research today-- it's too hard to disambiguate words in practice).
That's not all, of course-- try thinking of the need to deal with irony/sarcasm, metaphors, foreign words, the credibility of whichever sources you're using etc., and you'll get a pretty good feeling for why this is beyond merely being "hard". Of course, for very small problem domains (a "command language for naval vessels" was investigated in one paper I read a while ago-- those DARPA people definitely have too much money on their hands, but I digress), this can be solved, but general-purpose open-domain NLP is what you need to do a web search.
It might happen in my lifetime, but I won't hold my breath for it.
-- Christoph