Notebook and Steven Johnson...
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Notebook and Steven Johnson's "sweet spot"by ariew - 02:56PM, Aug 12, 2005 |
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Dear Fellow users,
Many of you are likely familiar with Steven Johnson's path-blazing article about using Mac software to enhance writing: http://www.stevenberlinjohnson.com/movabletype/archives/000230.html Essentially, the idea is to have a search engine go through clusters of text that are no longer than the 50-500 word range to show semantic relations between them that might uncover interesting discoveries that enhance a writer's project. He calls the 50-500 word cluster the "sweet spot" because any more (like whole articles) is too much information to garner interesting thoughts or connections. Johnson fantasizes about software that might break-up texts (books or articles) into clusters suitable for semantic searching. Here's the quote: "I wonder whether it might be possible to have software create those smaller clippings on its own: you'd feed the program an entire e-book, and it would break it up into 200-1000 word chunks of text, based on word frequency and other cues (chapter or section breaks perhaps.) Already Devonthink can take a large collection of documents and group them into categories based on word use, so theoretically you could do the same kind of auto-classification within a document. It still wouldn't have the pre-filtered property of my curated quotations, but it would make it far more productive to just dump a whole eBook into my digital research library." Well, it seems to me that Notebook can fulfill this function through its indexing feature. Paste or import a full article (say, 'paste text as outline' in the 'Edit' menu--which separates paragraphs as separate cells). Then, in the index, look for links to text that contain key words, words that you think might refer to an important or central idea. Then, go through the text and clip any relevant quote into your semantic search program (Johnson uses DevonThink). I wonder if anyone has worked with this feature. Any success? Any ideas to modify the process? |
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I'm sure you remember our discussion of this matter in the DevonThink forums. At the time, I was preoccupied with transferring mega-PDFs (i.e. whole journal issues) into DT Pro and I wanted to split them up before transfer. The discussion turned into one about software utilities to make it possible to process PDFs, and perhaps your essential point was lost in the process. I've revisited your post, and I tried out your method on an article I found on the web. I thought I'd share some of my thoughts while they are fresh. Since I was simply collecting snippets for storage in DevonThink Pro, I created a new, temporary notebook. As you suggested, selecting the whole article and using Paste Text as an Outline worked well with a webpage, each paragraph appearing as one cell. Using SuperFind on a keyword of special interest found five paragraphs containing the keyword. There is no way that I could find of expanding the found cells on the superfind results page, so I visited each cell, copied it, and pasted it into another temporary notebook. So that I would know where each cell had come from, I copied and pasted title, author, etc. into each cell. Then I selected the contents of each of the 5 cells in turn, and used Services to create a new Rich Note in DT Pro. Finally, I went to DT Pro and grouped these new rich texts. It seems to me that whether or not this is worthwhile depends to some extent on one's willingness to tolerate repetitive actions with the computer, or ability to tweak workflows to make things more efficient. The process wasn't exactly long-winded, but nor was it quite as quick as I had hoped. The two main bottlenecks were the SuperFind result (couldn't just open all the foubnd cells on the SuperFInd page but had to visit each one for manual copy) and the fact that, since the article is being broken up into chunks, info to identify its provenance has to be manually inserted into each chunk. During the time taken to extract these paragraphs, I could of course have actually been reading the article. On the other hand, if I'm going to be referring to the article in years to come, I'd have to store some info about it anyway and that would take some amount of time. Maybe it makes sense to do some preprocessing like this (in addition to storing the whole article). What is more exciting is the possibility, where normally we might start by reading 2 or 3 seminal articles on a subject, of collecting 10 or 20 initially, processing them in this way (and presumably the process would go faster if one did it repetitively), then reading through the selected paragraphs to get a focused but broad view, before working in a more normal way. I'd love to hear from anyone else who has tried working in this way, as well as suggestions for tweaking the detailed workflow. -Rick
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rickl
Member
01:01AM, May 11, 2006