As a result of lurking in a mailing list’s conversation about whether and how to translate Heidegger’s use of the ancient Greek term φυσις, I did some poking around at Google.

φυσις does not translate easily, which is why Heidegger scholars like to use the original Greek. (Meanwhile, I can’t even find an html character for the upsilon with a diacritical, and the raw Greek character failed in the preview of this post in Chrome.) It’s usually translated as “nature,” but that’s the result of a 2,500-year-old-game of “Telephone.” For Heidegger, it has something to do with what shows itself as having its own way of becoming or emerging. Richard Polt aand Gregory Fried in A Companion to Heidegger’s Introduction to Metaphysics take a stab at it by referring to it as the “emerging-abiding sway.” Anyway, that’s not the point of this post.

Here are the results. Have fun making sense of them. They are wonky in ways that indicate that I don’t know how to do Google queries.

Semi-interesting factoids based upon faulty research and poor quantitative reasoning skils:

• Hardly anyone who uses the Greek bothers to point out that there are two ways to transliterate it.

• A fifth of all mentions of the Greek term also mention Heidegger.

• If a work mentions Heidegger and the Greek term, it’s three times more likely to transliterate it as physis.

Fun minigame: How many of those did I mess up?

Google’s search syntax documentation is not great, and the results sometimes seem wonky. Here’s some documentation:

• Google’s documentation
• Beyond’s helpful documentation
• MIT’s documentation of delimiters not mentioned on that page.

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TED has started a great new project: Distributed translations of TED Talks. Taking a page from Global Voices, it’s crowd-sourcing translations.

This is exactly what should happen and is a great solution for relatively scarce resources such as TED talks. Figure out how to scale this and get yourself a Nobel prize.

By the way, TED has also introduced interactive transcripts: Click on a phrase in the transcript and the video skips to that spot. Very useful. And with a little specialized text editor, we could have the edit-video-by-editing-text app that I’ve been looking for.

[Tags: ted_talks translation crowdsourcing global_voices globalism everything_is_miscellaneous ]

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Seb Schmoller points to a terrific article by Google’s Alon Halevy, Peter Norvig, and Fernando Pereira about two ways to get meaning out of information. Their example is machine translation of natural language where there is so much translated material available for computers to learn from, which (they argue) works better than trying to learn from attempts that go up a level of abstraction and try to categorize and conceptualize the language. Scale wins. Or, as the article says, “But invariably, simple models and a lot of data trump more elaborate models based on less data.”

They then use this to distinguish the Semantic Web from “Semantic Interpretation.” The latter “deals with imprecise, ambiguous natural languages,” as opposed to aiming at data and application interoperability. “The problem of semantic interpretation remains: using a Semantic Web formalism just means that semantic interpretation must be done on shorter strings that fall between angle brackets.” Oh snap! “What we need are methods to infer relationships between column headers or mentions of entities in the world.” “Web-scale data” to the rescue! This is basic the same problem as translating from one language to another, given a large enough corpus of translations: We have a Web-scale collection of tables with column headers and content, so we should be able to algorithmically recognize clustering concordances of meaning.

I’m not doing the paper justice because I can’t, although it’s written quite clearly. But I find it fascinating. [Tags: data everything_is_miscellaneous natural_language translation semantic_web google ]

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