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The difference between electronic text and paper text

Could not think of a better way to talk about text except inviting Ariel Malka of Chronotext to a Dialogue about text, code, time and space. Here's only a tiny bit of what he shared with me.
Text, Space, Time and Code

Super happy to have Ariel Malka of Chronotext (a growing collection of software experiments exploring the relation between text, space and time: take part in my Dialogues series.

Stay tuned for an interview where we will be going beyond the boundaries of text and code into a world where signs, symbols and ciphers converge ina continuous Web flow.


Search engines are our eyes in the digital world
This is a very short piece by +Omi Sido. It is packed with substance. And a metaphor.
I know that many would question the idea that we can look at a search engine as if it were our eyes, but still, in a way our search (and the algorithms that mediate it) is our knowledge discovery quest :)

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Opening up texts on machine learning to a general public.
A new publication model for research
Isn't it strange how research still gets written up in 8-page two-column serif font, as if anyone was going to actually read the paper in printed proceedings? Why is the interesting, rich data relegated to the 'supplementary material' section (when it even exists)? Why is it so hard to refer to online resources and code? Why do many authors today feel the need to provide an accompanying website or blog post?
Let's make that website the centerpiece instead! Distill is a new way to publish research which provides a much richer authoring model and tools to help researchers communicate their work better. The journal is entirely designed to live on the web, is peer reviewed and registered with the Library of Congress and CrossRef. The constraints: a high bar for content quality, clarity and educational value. Go check it out!

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The Hero's Journey, Parsed by Big Data Analysis

They examined 1,327 stories from Project Gutenberg’s fiction collection — all English-language texts between 20,000 and 100,000 words — using three language processing filters. In the end, they found “broad support for the following six emotional arcs”:
* Rags to riches (rise)
* Tragedy, or riches to rags (fall)
* Man in a hole (fall-rise)
* Icarus (rise-fall)
* Cinderella (rise-fall-rise)
* Oedipus (fall-rise-fall)

HT +Pamela Pavliscak

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LSI: A Method of Keyword Indexing That Matches User Intent

I am fascinating with Latent Semantic Indexing Keywords as I learn more about it. Its more advances indexing and retrieval method for semantic search has been around for awhile but is advancing.

Latent semantic indexing adds an additional key step to search’s document indexing process. On top of detailing which keywords a document is comprised of, this approach inspects the document collection as a whole, to determine which additional content contain some of those same words. LSI deems documents that have numerous words in common to be semantically close, and ones containing scarcely any common keywords to be semantically distant. This simple method relates amazing fine with how a person, looking at content, may categorize a document collection.

Latent Semantic Indexing (LSI) seems to have become a fixed aspect of the Google algorithm. It is important to stay current with changes to win in semantic indexing. Although the LSI algorithm doesn’t comprehend that least about what the words mean, the patterns it perceives grant the algorithm an astonishingly level of intelligence.

“When you search an LSI-indexed database, the search engine looks at similarity values it has calculated for every content word, and returns the documents that it thinks best fit the query. Because two documents may be semantically very close even if they do not share a particular keyword, LSI does not require an exact match to return useful results,” states SEO Book.

In contrast, I find it quickly outpaces ordinary keyword search, which is left in a blank state if there is no exact match. In the same situation, LSI will is capable of returning relevant content that only relate by association and match intent.

It seems that the expected growth in semantic search in 2016 is solid and growing.

More information at:


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The reason I am adding this here is because AI and machine learning are changing the web writing landscape. Machines are increasingly becoming content consumers in one way or another.
Google's AI Experiments

Familiarize yourself with some of the practical applications of AI and machine learning with a new Google initiative.

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A new approach to information extraction with AI
Algorithms are getting more advanced. This is an example from the field of information extraction where an algorithm specifically starts learning about elements he is uncertain about by searching the web.

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A new approach to information extraction with AI
Algorithms are getting more advanced. This is an example from the field of information extraction where an algorithm specifically starts learning about elements he is uncertain about by searching the web.

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Study sheds new light on Hummingbird

I thought Hummingbird was water under the bridge. But +Neil Patel just published results of an extensive study that yielded a few surprises (at least to me):

1. A Hummingbird-optimized site will outrank a huge link profile.

2. Topical gaps hurt your site authority and rankings, and they're not as obvious as you might think. A topical gap is caused by not mentioning tangential items that people usually think of in conjunction with your subject, such as "gas mileage" and "fuel economy" when the website's subject is "saving money."

3. Deep niche content works best with Hummingbird. (No surprise there.) But beyond that, it's important not to expand your topics unless you've gone in-depth enough to establish authority in your focused main topic.

4. The text on your homepage can be the most important on your website. Neil says the search engines will look at the home page and your About page the most closely to determine your subject focus.

Does anyone else find these things enlightening, as I do?

Read all his findings here:
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